GAPS IN SUPPORT VECTOR OPTIMIZATION
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STEINWART, INGO [Los Alamos National Laboratory; HUSH, DON [Los Alamos National Laboratory; SCOVEL, CLINT [Los Alamos National Laboratory; LIST, NICOLAS [Los Alamos National Laboratory
2007-01-29
We show that the stopping criteria used in many support vector machine (SVM) algorithms working on the dual can be interpreted as primal optimality bounds which in turn are known to be important for the statistical analysis of SVMs. To this end we revisit the duality theory underlying the derivation of the dual and show that in many interesting cases primal optimality bounds are the same as known dual optimality bounds.
Support vector machines optimization based theory, algorithms, and extensions
Deng, Naiyang; Zhang, Chunhua
2013-01-01
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi
Support Vector Machine Optimized by Improved Genetic Algorithm
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Xiang Chang Sheng
2013-07-01
Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.
Optimized support vector regression for drilling rate of penetration estimation
Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa
2015-12-01
In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.
Hybrid Optimization of Support Vector Machine for Intrusion Detection
Institute of Scientific and Technical Information of China (English)
XI Fu-li; YU Song-nian; HAO Wei
2005-01-01
Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it's an effective method and can improve the perfornance of SVM-based intrusion detection system further.
Optimization of Support Vector Machine (SVM) for Object Classification
Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin
2012-01-01
The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.
Hybrid Neural Network and Support Vector Machine Method for Optimization
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Yang, Xin-She; Fong, Simon
2012-01-01
Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.
Institute of Scientific and Technical Information of China (English)
TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun
2009-01-01
The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.
Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation
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Ching-Wei Chen
2016-01-01
Full Text Available This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM, the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found that R2 of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.
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Jianzhou Wang
2015-01-01
Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.
Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun
2016-01-01
Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
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Mohammed Hasan Abdulameer
2014-01-01
Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.
Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
An iterative (run-to-run) optimization method was presented for batch processes under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models were developed for the end-point optimization of batch processes. Since there is no analytical way to find the optimal trajectory, an iterative method was used to exploit the repetitive nature of batch processes to determine the optimal operating policy. The optimization algorithm is proved convergent. The numerical simulation shows that the method can improve the process performance through iterations.
Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration
Institute of Scientific and Technical Information of China (English)
Ratnakar Dash; Pankaj Kumar Sa; Banshidhar Majhi
2012-01-01
This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM)for blind image restoration.In this work,SVM is used to solve a regression problem.Support vector regression (SVR)has been utilized to obtain a true mapping of images from the observed noisy blurred images.The parameters of SVR are optimized through particle swarm optimization (PSO) technique.The restoration error function has been utilized as the fitness function for PSO.The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness.The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.
2015-01-01
Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...
Xiong, Yuhong; Liu, Yunxiang; Shu, Minglei
2016-10-01
In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.
Sahin, M. Ö.; Krücker, D.; Melzer-Pellmann, I.-A.
2016-12-01
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.
Parameter selection of support vector machine for function approximation based on chaos optimization
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The support vector machine (SVM) is a novel machine learning method,which has the ability to approximate nonlinear functions with arbitrary accuracy.Setting parameters well is very crucial for SVM learning results and generalization ability,and now there is no systematic,general method for parameter selection.In this article,the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values.The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy.Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling
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Qi Zhang
2013-11-01
Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.
Institute of Scientific and Technical Information of China (English)
Zhao Xiufen; Yin Guofu; Tian Guiyun; Yin Ying
2008-01-01
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft. A novel automatic defect identification system is presented. Wavelet packet analysis (WPA) was applied to feature extraction of ultrasonic signal, and optimal Support vector machine (SVM) was used to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models. To validate the method, some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
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Yukai Yao
2015-01-01
Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.
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Pan Jin-Shui
2009-05-01
Full Text Available Abstract Background Transfection in mammalian cells based on liposome presents great challenge for biological professionals. To protect themselves from exogenous insults, mammalian cells tend to manifest poor transfection efficiency. In order to gain high efficiency, we have to optimize several conditions of transfection, such as amount of liposome, amount of plasmid, and cell density at transfection. However, this process may be time-consuming and energy-consuming. Fortunately, several mathematical methods, developed in the past decades, may facilitate the resolution of this issue. This study investigates the possibility of optimizing transfection efficiency by using a method referred to as least-squares support vector machine, which requires only a few experiments and maintains fairly high accuracy. Results A protocol consists of 15 experiments was performed according to the principle of uniform design. In this protocol, amount of liposome, amount of plasmid, and the number of seeded cells 24 h before transfection were set as independent variables and transfection efficiency was set as dependent variable. A model was deduced from independent variables and their respective dependent variable. Another protocol made up by 10 experiments was performed to test the accuracy of the model. The model manifested a high accuracy. Compared to traditional method, the integrated application of uniform design and least-squares support vector machine greatly reduced the number of required experiments. What's more, higher transfection efficiency was achieved. Conclusion The integrated application of uniform design and least-squares support vector machine is a simple technique for obtaining high transfection efficiency. Using this novel method, the number of required experiments would be greatly cut down while higher efficiency would be gained. Least-squares support vector machine may be applicable to many other problems that need to be optimized.
Wu, Qi
2010-03-01
Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.
Support Vector Components Analysis
van der Ree, Michiel; Roerdink, Johannes; Phillips, Christophe; Garraux, Gaetan; Salmon, Eric; Wiering, Marco
2017-01-01
In this paper we propose a novel method for learning a distance metric in the process of training Support Vector Machines (SVMs) with the radial basis function kernel. A transformation matrix is adapted in such a way that the SVM dual objective of a classification problem is optimized. By using a wi
A new support vector machine optimized by improved particle swarm optimization and its application
Institute of Scientific and Technical Information of China (English)
LI Xiang; YANG Shang-dong; QI Jian-xun
2006-01-01
A new support vectormachine (SVM) optimized by an improved particle swarm optimization (PSO)combined with simulated annealing algorithm (SA) was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization(SAPSO) was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c, σ and ε). Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
Optimization of Filter by using Support Vector Regression Machine with Cuckoo Search Algorithm
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M. İlarslan
2014-09-01
Full Text Available Herein, a new methodology using a 3D Electromagnetic (EM simulator-based Support Vector Regression Machine (SVRM models of base elements is presented for band-pass filter (BPF design. SVRM models of elements, which are as fast as analytical equations and as accurate as a 3D EM simulator, are employed in a simple and efficient Cuckoo Search Algorithm (CSA to optimize an ultra-wideband (UWB microstrip BPF. CSA performance is verified by comparing it with other Meta-Heuristics such as Genetic Algorithm (GA and Particle Swarm Optimization (PSO. As an example of the proposed design methodology, an UWB BPF that operates between the frequencies of 3.1 GHz and 10.6 GHz is designed, fabricated and measured. The simulation and measurement results indicate in conclusion the superior performance of this optimization methodology in terms of improved filter response characteristics like return loss, insertion loss, harmonic suppression and group delay.
Sahin, Mehmet Özgür; Melzer-Pellmann, Isabell-Alissandra
2016-01-01
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.
Energy Technology Data Exchange (ETDEWEB)
Sahin, Mehmet Oezguer; Kruecker, Dirk; Melzer-Pellmann, Isabell [DESY, Hamburg (Germany)
2016-07-01
In this talk, the use of Support Vector Machines (SVM) is promoted for new-physics searches in high-energy physics. We developed an interface, called SVM HEP Interface (SVM-HINT), for a popular SVM library, LibSVM, and introduced a statistical-significance based hyper-parameter optimization algorithm for the new-physics searches. As example case study, a search for Supersymmetry at the Large Hadron Collider is given to demonstrate the capabilities of SVM using SVM-HINT.
Energy Technology Data Exchange (ETDEWEB)
Sahin, M.Oe.; Kruecker, D.; Melzer-Pellmann, I.A.
2016-01-15
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.
Fruit fly optimization based least square support vector regression for blind image restoration
Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei
2014-11-01
The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and
Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
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Chenzhong Cao
2009-07-01
Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.
Process Optimization of Ultrasonic Extraction of Puerarin Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
Juan Chen; Xiaoyi Huang; Yanlei Qi; Xin Qi; Qing Guo
2014-01-01
In ultrasonic extraction technology, optimization of technical parameters often considers extraction medium only, without including ultrasonic parameters. This paper focuses on controlling the ultrasonic extraction process of puerarin, investigating the influence of ultrasonic parameters on extraction rate, and empirical y analyzing the main components of Pueraria, i.e., isoflavone compounds. A method is presented combining orthogonal experi-mental design with a support vector machine and a predictive model is established for optimization of technical parameters. From the analysis with the predictive model, appropriate process parameters are achieved for higher extraction rate. With these parameters in the ultrasonic extraction of puerarin, the experimental result is satisfactory. This method is of significance to the study of extracting root-stock plant medicines.
Kernel Learning in Support Vector Machines using Dual-Objective Optimization
Pietersma, Auke-Dirk; Schomaker, Lambertus; Wiering, Marco
2011-01-01
Support vector machines (SVMs) are very popular methods for solving classification problems that require mapping input features to target labels. When dealing with real-world data sets, the different classes are usually not linearly separable, and therefore support vector machines employ a particula
ch, Sudheer; Kumar, Deepak; Prasad, Ram Kailash; Mathur, Shashi
2013-08-01
A methodology based on support vector machine and particle swarm optimization techniques (SVM-PSO) was used in this study to determine an optimal pumping rate and well location to achieve an optimal cost of an in-situ bioremediation system. In the first stage of the two stage methodology suggested for optimal in-situ bioremediation design, the optimal number of wells and their locations was determined from preselected candidate well locations. The pumping rate and well location in the first stage were subsequently optimized in the second stage of the methodology. The highly nonlinear system of equations governing in-situ bioremediation comprises the equations of flow and solute transport coupled with relevant biodegradation kinetics. A finite difference model was developed to simulate the process of in-situ bioremediation using an Alternate-Direction Implicit technique. This developed model (BIOFDM) yields the spatial and temporal distribution of contaminant concentration for predefined initial and boundary conditions. BIOFDM was later validated by comparing the simulated results with those obtained using BIOPLUME III for the case study of Shieh and Peralta (2005). The results were found to be in close agreement. Moreover, since the solution of the highly nonlinear equation otherwise requires significant computational effort, the computational burden in this study was managed within a practical time frame by replacing the BIOFDM model with a trained SVM model. Support Vector Machine which generates fast solutions in real time was considered to be a universal function approximator in the study. Apart from reducing the computational burden, this technique generates a set of near optimal solutions (instead of a single optimal solution) and creates a re-usable data base that could be used to address many other management problems. Besides this, the search for an optimal pumping pattern was directed by a simple PSO technique and a penalty parameter approach was adopted
ch, Sudheer; Kumar, Deepak; Prasad, Ram Kailash; Mathur, Shashi
2013-08-01
A methodology based on support vector machine and particle swarm optimization techniques (SVM-PSO) was used in this study to determine an optimal pumping rate and well location to achieve an optimal cost of an in-situ bioremediation system. In the first stage of the two stage methodology suggested for optimal in-situ bioremediation design, the optimal number of wells and their locations was determined from preselected candidate well locations. The pumping rate and well location in the first stage were subsequently optimized in the second stage of the methodology. The highly nonlinear system of equations governing in-situ bioremediation comprises the equations of flow and solute transport coupled with relevant biodegradation kinetics. A finite difference model was developed to simulate the process of in-situ bioremediation using an Alternate-Direction Implicit technique. This developed model (BIOFDM) yields the spatial and temporal distribution of contaminant concentration for predefined initial and boundary conditions. BIOFDM was later validated by comparing the simulated results with those obtained using BIOPLUME III for the case study of Shieh and Peralta (2005). The results were found to be in close agreement. Moreover, since the solution of the highly nonlinear equation otherwise requires significant computational effort, the computational burden in this study was managed within a practical time frame by replacing the BIOFDM model with a trained SVM model. Support Vector Machine which generates fast solutions in real time was considered to be a universal function approximator in the study. Apart from reducing the computational burden, this technique generates a set of near optimal solutions (instead of a single optimal solution) and creates a re-usable data base that could be used to address many other management problems. Besides this, the search for an optimal pumping pattern was directed by a simple PSO technique and a penalty parameter approach was adopted
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Yu Wang
2015-01-01
Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.
Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines
Institute of Scientific and Technical Information of China (English)
WANG Zhifei; WANG Hua
2012-01-01
The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels.To overcome some of the defects of the inflatable wing parameter design method,this paper proposes an optimization design scheme based on orthogonal testing and support vector machines (SVMs).Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency.Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure,uniform applied load and the number of chambers that affect the bending response of inflatable wings.An SVM intelligent model is established and limited orthogonal test swatches are studied.Thus,the precise relationships between each parameter and product quality features,as well the signal-to-noise ratio (SNR),can be obtained.This can guide general technological design optimization.
Directory of Open Access Journals (Sweden)
Yudong Zhang
2013-01-01
Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.
Maximum likelihood optimal and robust Support Vector Regression with lncosh loss function.
Karal, Omer
2017-10-01
In this paper, a novel and continuously differentiable convex loss function based on natural logarithm of hyperbolic cosine function, namely lncosh loss, is introduced to obtain Support Vector Regression (SVR) models which are optimal in the maximum likelihood sense for the hyper-secant error distributions. Most of the current regression models assume that the distribution of error is Gaussian, which corresponds to the squared loss function and has helpful analytical properties such as easy computation and analysis. However, in many real world applications, most observations are subject to unknown noise distributions, so the Gaussian distribution may not be a useful choice. The developed SVR model with the parameterized lncosh loss provides a possibility of learning a loss function leading to a regression model which is maximum likelihood optimal for a specific input-output data. The SVR models obtained with different parameter choices of lncosh loss with ε-insensitiveness feature, possess most of the desirable characteristics of well-known loss functions such as Vapnik's loss, the Squared loss, and Huber's loss function as special cases. In other words, it is observed in the extensive simulations that the mentioned lncosh loss function is entirely controlled by a single adjustable λ parameter and as a result, it allows switching between different losses depending on the choice of λ. The effectiveness and feasibility of lncosh loss function are validated through a number of synthetic and real world benchmark data sets for various types of additive noise distributions. Copyright © 2017 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Chun-Liang Lu
2015-10-01
Full Text Available The optimized hybrid artificial intelligence model is a potential tool to deal with construction engineering and management problems. Support vector machine (SVM has achieved excellent performance in a wide variety of applications. Nevertheless, how to effectively reduce the training complexity for SVM is still a serious challenge. In this paper, a novel order-independent approach for instance selection, called the dynamic condensed nearest neighbor (DCNN rule, is proposed to adaptively construct prototypes in the training dataset and to reduce the redundant or noisy instances in a classification process for the SVM. Furthermore, a hybrid model based on the genetic algorithm (GA is proposed to simultaneously optimize the prototype construction and the SVM kernel parameters setting to enhance the classification accuracy. Several UCI benchmark datasets are considered to compare the proposed hybrid GA-DCNN-SVM approach with the previously published GA-based method. The experimental results illustrate that the proposed hybrid model outperforms the existing method and effectively improves the classification performance for the SVM.
Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
Directory of Open Access Journals (Sweden)
Utku Kose
2016-03-01
Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.
Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
Directory of Open Access Journals (Sweden)
Utku Kose
2016-03-01
Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.
Directory of Open Access Journals (Sweden)
S.K. Lahiri
2009-09-01
Full Text Available Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR, a new powerful machine learning methodbased on a statistical learning theory (SLT into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression – genetic algorithm (SVR-GA for modeling and optimization of mono ethylene glycol (MEG quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc. is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.
Fast Training of Support Vector Machines Using Error-Center-Based Optimization
Institute of Scientific and Technical Information of China (English)
L. Meng; Q. H. Wu
2005-01-01
This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments withvarious training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques.
Support vector machines applications
Guo, Guodong
2014-01-01
Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.
Fuzzy Support Vector Machine-based Multi-agent Optimal Path
Directory of Open Access Journals (Sweden)
Gireesh Kumar T
2010-07-01
Full Text Available A mobile robot to navigate purposefully from a start location to a target location, needs three basic requirements: sensing, learning, and reasoning. In the existing system, the mobile robot navigates in a known environment on a predefined path. However, the pervasive presence of uncertainty in sensing and learning, makes the choice of a suitable tool of reasoning and decision-making that can deal with incomplete information, vital to ensure a robust control system. This problem can be overcome by the proposed navigation method using fuzzy support vector machine (FSVM. It proposes a fuzzy logic-based support vector machine (SVM approach to secure a collision-free path avoiding multiple dynamic obstacles. The navigator consists of an FSVM-based collision avoidance. The decisions are taken at each step for the mobile robot to attain the goal position without collision. Fuzzy-SVM rule bases are built, which require simple evaluation data rather than thousands of input-output training data. The effectiveness of the proposed method is verified by a series of simulations and implemented with a microcontroller for navigation.Defence Science Journal, 2010, 60(4, pp.387-391, DOI:http://dx.doi.org/10.14429/dsj.60.496
Directory of Open Access Journals (Sweden)
Xiaomin Xu
2015-01-01
Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.
Duality in vector optimization
Bot, Radu Ioan
2009-01-01
This book presents fundamentals and comprehensive results regarding duality for scalar, vector and set-valued optimization problems in a general setting. After a preliminary chapter dedicated to convex analysis and minimality notions of sets with respect to partial orderings induced by convex cones a chapter on scalar conjugate duality follows. Then investigations on vector duality based on scalar conjugacy are made. Weak, strong and converse duality statements are delivered and connections to classical results from the literature are emphasized. One chapter is exclusively consecrated to the s
Energy Technology Data Exchange (ETDEWEB)
Feng Wu; Hao Zhou; Tao Ren; Ligang Zheng; Kefa Cen [Zhejiang University, Hangzhou (China). State Key Laboratory of Clean Energy Utilization
2009-10-15
Support vector regression (SVR) was employed to establish mathematical models for the NOx emissions and carbon burnout of a 300 MW coal-fired utility boiler. Combined with the SVR models, the cellular genetic algorithm for multi-objective optimization (MOCell) was used for multi-objective optimization of the boiler combustion. Meanwhile, the comparison between MOCell and the improved non-dominated sorting genetic algorithm (NSGA-II) shows that MOCell has superior performance to NSGA-II regarding the problem. The field experiments were carried out to verify the accuracy of the results obtained by MOCell, the results were in good agreement with the measurement data. The proposed approach provides an effective tool for multi-objective optimization of coal combustion performance, whose feasibility and validity are experimental validated. A time period of less than 4 s was required for a run of optimization under a PC system, which is suitable for the online application. 19 refs., 8 figs., 2 tabs.
Boosting Support Vector Machines
Directory of Open Access Journals (Sweden)
Elkin Eduardo García Díaz
2006-11-01
Full Text Available En este artículo, se presenta un algoritmo de clasificación binaria basado en Support Vector Machines (Máquinas de Vectores de Soporte que combinado apropiadamente con técnicas de Boosting consigue un mejor desempeño en cuanto a tiempo de entrenamiento y conserva características similares de generalización con un modelo de igual complejidad pero de representación más compacta./ In this paper we present an algorithm of binary classification based on Support Vector Machines. It is combined with a modified Boosting algorithm. It run faster than the original SVM algorithm with a similar generalization error and equal complexity model but it has more compact representation.
Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin
2016-01-15
Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods.
Energy Technology Data Exchange (ETDEWEB)
Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)
2009-06-15
Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)
Energy Technology Data Exchange (ETDEWEB)
Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)
2009-06-15
Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Wu, Hai-wei; Yu, Hai-ye; Zhang, Lei
2011-05-01
Using K-fold cross validation method and two support vector machine functions, four kernel functions, grid-search, genetic algorithm and particle swarm optimization, the authors constructed the support vector machine model of the best penalty parameter c and the best correlation coefficient. Using information granulation technology, the authors constructed P particle and epsilon particle about those factors affecting net photosynthetic rate, and reduced these dimensions of the determinant. P particle includes the percent of visible spectrum ingredients. Epsilon particle includes leaf temperature, scattering radiation, air temperature, and so on. It is possible to obtain the best correlation coefficient among photosynthetic effective radiation, visible spectrum and individual net photosynthetic rate by this technology. The authors constructed the training set and the forecasting set including photosynthetic effective radiation, P particle and epsilon particle. The result shows that epsilon-SVR-RBF-genetic algorithm model, nu-SVR-linear-grid-search model and nu-SVR-RBF-genetic algorithm model obtain the correlation coefficient of up to 97% about the forecasting set including photosynthetic effective radiation and P particle. The penalty parameter c of nu-SVR-linear-grid-search model is the minimum, so the model's generalization ability is the best. The authors forecasted the forecasting set including photosynthetic effective radiation, P particle and epsilon particle by the model, and the correlation coefficient is up to 96%.
Directory of Open Access Journals (Sweden)
Maolong Xi
2016-01-01
Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
Xi, Maolong; Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun
2016-01-01
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
Directory of Open Access Journals (Sweden)
Bao Wang
2012-11-01
Full Text Available The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA, generalized regression neural network (GRNN and regression model.
Energy Technology Data Exchange (ETDEWEB)
Ligang Zheng; Hao Zhou; Chunlin Wang; Kefa Cen [Zhejiang University, Hangzhou (China). State Key Laboratory of Clean Energy Utilization
2008-03-15
Combustion optimization has recently demonstrated its potential to reduce NOx emissions in high capacity coal-fired utility boilers. In the present study, support vector regression (SVR), as well as artificial neural networks (ANN), was proposed to model the relationship between NOx emissions and operating parameters of a 300 MW coal-fired utility boiler. The predicted NOx emissions from the SVR model, by comparing with that of the ANN-based model, showed better agreement with the values obtained in the experimental tests on this boiler operated at different loads and various other operating parameters. The mean modeling error and the correlation factor were 1.58% and 0.94, respectively. Then, the combination of the SVR model with ant colony optimization (ACO) to reduce NOx emissions was presented in detail. The experimental results showed that the proposed approach can effectively reduce NOx emissions from the coal-fired utility boiler by about 18.69% (65 ppm). A time period of less than 6 min was required for NOx emissions modeling, and 2 min was required for a run of optimization under a PC system. The computing times are suitable for the online application of the proposed method to actual power plants. 37 refs., 8 figs., 3 tabs.
New approach to training support vector machine
Institute of Scientific and Technical Information of China (English)
Tang Faming; Chen Mianyun; Wang Zhongdong
2006-01-01
Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.
Institute of Scientific and Technical Information of China (English)
ZENG Ming; LU Chunquan; TIAN Kuo; XUE Song
2011-01-01
During the Twelfth Five-Year plan, large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method. Moreover, along with the full-scale smart grid construction, the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting; and the security, stability and cleanness of the system can be guaranteed.
Directory of Open Access Journals (Sweden)
Yi Liang
2016-10-01
Full Text Available Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT and least squares support vector machine (LSSVM, which is optimized by an improved cuckoo search (CS. To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Directory of Open Access Journals (Sweden)
Chui-Yu Chiu
2013-08-01
Full Text Available Patent rights have the property of exclusiveness. Inventors can protect their rights in the legal range and have monopoly for their open inventions. People are not allowed to use an invention before the inventors permit them to use it. Companies try to avoid the research and development investment in inventions that have been protected by patent. Patent retrieval and categorization technologies are used to uncover patent information to reduce the cost of torts. In this research, we propose a novel method which integrates the Honey-Bee Mating Optimization algorithm with Support Vector Machines for patent categorization. First, the CKIP method is utilized to extract phrases of the patent summary and title. Then we calculate the probability that a specific key phrase contains a certain concept based on Term Frequency - Inverse Document Frequency (TF-IDF methods. By combining frequencies and the probabilities of key phases generated by using the Honey-Bee Mating Optimization algorithm, our proposed method is expected to obtain better representative input values for the SVM model. Finally, this research uses patents from Chemical Mechanical Polishing (CMP as case examples to illustrate and demonstrate the superior results produced by the proposed methodology.
Zhang, Chang-Jiang; Dai, Li-Jie; Ma, Lei-Ming
2016-10-01
The data of current PM2.5 model forecasting greatly deviate from the measured concentration. In order to solve this problem, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) are combined to build a rolling forecasting model. The important parameters (C and γ) of SVM are optimized by PSO. The data (from February to July in 2015), consisting of measured PM2.5 concentration, PM2.5 model forecasting concentration and five main model forecasting meteorological factors, are provided by Shanghai Meteorological Bureau in Pudong New Area. The rolling model is used to forecast hourly PM2.5 concentration in 12 hours in advance and the nighttime average concentration (mean value from 9 pm to next day 8 am) during the upcoming day. The training data and the optimal parameters of SVM model are different in every forecasting, that is to say, different models (dynamic models) are built in every forecasting. SVM model is compared with Radical Basis Function Neural Network (RBFNN), Multi-variable Linear Regression (MLR) and WRF-CHEM. Experimental results show that the proposed model improves the forecasting accuracy of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day. SVM model performs better than MLR, RBFNN and WRF-CHEM. SVM model greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance, according with the result concluded from previous research. The rolling forecasting model can be applied to the field of PM2.5 concentration forecasting, and can offer help to meteorological administration in PM2.5 concentration monitoring and forecasting.
Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong
2014-08-01
Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.
Hao, Yong; Liu, Yande; Zhang, Hailiang; Liu, Xuemei; Pan, Yuanyuan
2010-10-01
In this study, Visible/near-infrared (Vis/NIR) diffuse reflectance spectroscopy at 530-1560 nm region was investigated for the analysis of the soluble solids content (SSC) and color of pear. Least squares support vector regression (LSSVR) has been proven to be a powerful tool for modeling complex samples through the use of adapted kernel functions. However, one of the major drawbacks of LSSVR is that the optimization of the regularization and kernel meta-parameters is time-consuming during training the model, and the modeling results are sensitive to spectral noise. Wavelet compression pretreatment is an effective method for spectral information extraction and noise elimination. The calibration set was composed of 75 pear samples and 32 pear samples were used as the validation set. The raw and pretreated spectra by wavelet compression were modeled using LSSVR, It was shown that wavelet compression procedure not only shortened the modeling time, but also improved the predictive precision. The correlation coefficient (r) was improved from 0.78 to 0.93 for SSC, and from 0.95 to 0.96 for color, respectively. The root mean square error of prediction (RMSEP), optimization time and calibration variables were reduced from 0.68, 0.33s and 1031 to 0.41, 0.03s and 24 for SSC, while from 1.10, 0.33s and 1031 to 1.07, 0.03s and 40 for color. The results indicated that Vis/NIR spectroscopy combined with wavelet compression procedure and LSSVR is a reliable approach for predicting the SSC and color of pear.
The Neural Support Vector Machine
Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus
2013-01-01
This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a
The Neural Support Vector Machine
Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus
2013-01-01
This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a centr
The Duality on Vector Optimization Problems
Institute of Scientific and Technical Information of China (English)
HUANG Long-guang
2012-01-01
Duality framework on vector optimization problems in a locally convex topological vector space are established by using scalarization with a cone-strongly increasing function.The dualities for the scalar convex composed optimization problems and for general vector optimization problems are studied.A general approach for studying duality in vector optimization problems is presented.
Directory of Open Access Journals (Sweden)
Wei Sun
2015-01-01
Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Institute of Scientific and Technical Information of China (English)
Jiahuan Wu; Jianlin Wang; Tao Yu; Liqiang Zhao
2014-01-01
The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making (DM) procedure, in which the continuous approximation of Pareto front and decision-making is performed interactive-ly, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition, combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experi-mental results show that the generated approximate continuous Pareto front has good accuracy and complete-ness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less compu-tation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.
Bifurcations of optimal vector fields
Kiseleva, T.; Wagener, F.
2015-01-01
We study the structure of the solution set of a class of infinite-horizon dynamic programming problems with one-dimensional state spaces, as well as their bifurcations, as problem parameters are varied. The solutions are represented as the integral curves of a multivalued optimal vector field on sta
Directory of Open Access Journals (Sweden)
Reza Azad
2013-11-01
Full Text Available Automatic face recognition system is one of the core technologies in computer vision, machine learning, and biometrics. The present study presents a novel and improved way for face recognition. In the suggested approach, first, the place of face is extracted from the original image and then is sent to feature extraction stage, which is based on Principal Component Analysis (PCA technique. In the previous procedures which were established on PCA technique, the whole picture was taken as a vector feature, then among these features, key features were extracted with use of PCA algorithm, revealing finally some poor efficiency. Thus, in the recommended approach underlying the current investigation, first the areas of face features are extracted; then, the areas are combined and are regarded as vector features. Ultimately, its key features are extracted with use of PCA algorithm. Taken together, after extracting the features, for face recognition and classification, Multiclass Support Vector Machine (SVMs classifiers, which are typical of high efficiency, have been employed. In the result part, the proposed approach is applied on FEI database and the accuracy rate achieved 98.45%.
Directory of Open Access Journals (Sweden)
Jinshui Zhang
2017-04-01
Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.
Zhang, Jinshui; Yuan, Zhoumiqi; Shuai, Guanyuan; Pan, Yaozhong; Zhu, Xiufang
2017-04-26
This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C) and kernel width (s), in mapping homogeneous specific land cover.
Learning with Support Vector Machines
Campbell, Colin
2010-01-01
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a
Support vector machine applied in QSAR modelling
Institute of Scientific and Technical Information of China (English)
MEI Hu; ZHOU Yuan; LIANG Guizhao; LI Zhiliang
2005-01-01
Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural network (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) regression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel function. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.
Dai, C; Li, Y P; Huang, G H
2011-12-01
In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands.
Yu, Yang; Li, Yancheng; Li, Jianchun
2015-03-01
Due to its inherent hysteretic characteristics, the main challenge for the application of a magnetorheological elastomer- (MRE) based isolator is the exploitation of the accurate model, which could fully describe its unique behaviour. This paper proposes a nonparametric model for a MRE-based isolator based on support vector regression (SVR). The trained identification model is to forecast the shear force of the MRE-based isolator online; thus, the dynamic response from the MRE-based isolator can be well captured. In order to improve the forecast capacity of the model, a type of improved particle swarm optimization (IPSO) is employed to optimize the parameters in SVR. Eventually, the trained model is applied to the MRE-based isolator modelling with testing data. The results indicate that the proposed hybrid model has a better generalization capacity and better recognition accuracy than other conventional models, and it is an effective and suitable approach for forecasting the behaviours of a MRE-based isolator.
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
A Survey of Optimization Algorithms in Support Vector Machine%支持向量机中优化算法
Institute of Scientific and Technical Information of China (English)
宋晓峰; 陈德钊; 俞欢军; 胡上序
2003-01-01
Optimization algorithm solving Lagrangian multipliers is the key of training SVM,determining the perfor-mance of SVM ,affecting practical applications of SVM in various fields widely. Some kinds of optimization algorithmsin SVM of overseas are introduced. We classify the optimization algorithms into two kinds: 1. the algorithms based onOsuna's decomposition strategy; 2. The iterative algorithms based on the changes of SVM formulation proposed byO. L. Mangasarian. We also analyze the characteristics of various optimization algorithms in SVM ,and predicting thetrend of research on optimization algorithm in SVM.
Directory of Open Access Journals (Sweden)
Chen Wang
2016-01-01
Full Text Available Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD, which reduces the effect of noise on the wind speed data; (II artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM model are optimized by the cuckoo search (CS algorithm; (III parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.
Adjustable entropy function method for support vector machine
Institute of Scientific and Technical Information of China (English)
Wu Qing; Liu Sanyang; Zhang Leyou
2008-01-01
Based on KKT complementary condition in optimization theory,an unconstrained non-differential optimization model for support vector machine is proposed.An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution.The proposed method can find an optimal solution with a relatively small parameter p,which avoids the numerical overflow in the traditional entropy function methods.It is a new approach to solve support vector machine.The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.
Institute of Scientific and Technical Information of China (English)
龚松建; 袁宇浩; 王莉; 张广明
2011-01-01
提出了一种基于粒子群(PSO)算法优化最小二乘支持向量机(LS-SVM)的风电场风速预测方法.以相关性较高的历史风速序列作为输人,建立预测模型,并用粒子群算法优化模型参数.在对未来1h风速进行预测时,文章所提出的模型比最小二乘支持向量机模型及BP神经网络模型具有较高的预测精度和运算速度.算例结果表明,经粒子群优化的最小二乘支持向量机算法是进行短期风速预测的有效方法.%A wind speed forecasting for wind farm based on least squares support vector machine optimized by particle swarm optimization algorithm is proposed.Taking historical wind speed data which have higher correlation as the input, then a forecasting model is built, and by use of particle swarm optimization, the parameters of the model are determined.In the one hour wind speed forecasting of this wind farm,the proposed wind speed model is compared with wind speed model based on least squares support vector machine (LS-SVM) and that based on back propagation neural network, the comparison results show that the proposed wind speed predicting model is better than these two models in both prediction accuracy and computing speed.The simulation results show that the least squares support vector machine optimized by particle swarm optimization algorithm is an effective method for short-term wind forecasting.
基于支持向量机的序列可靠性优化方法%Sequential reliability-based optimization with support vector machines
Institute of Scientific and Technical Information of China (English)
王宇; 余雄庆; 杜小平
2013-01-01
T raditional reliability-based design optimization (RBDO ) is either computational intensive or not accurate enough .In this work ,a new RBDO method based on Support Vector Machines (SVM ) is proposed .For reliability analysis ,SVM is used to create a surrogate model of the limit-state function at the Most Probable Point (MPP) .The uniqueness of the new method is the use of the gradient of the lim-it-state function at the MPP .This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP .Then Importance Sampling (IS) is used to cal-culate the probability of failure based on the surrogate model .This treatment significantly improves the accuracy of reliability analysis .For optimization ,the Sequential Optimization and Reliability Assessment (SORA) is employed ,which decouples deterministic optimization from the SVM reliability analysis .The decoupling makes RBDO more efficient .The two examples show that the new method is more accurate with a moderately increased computational cost .%在工程设计中，可靠性优化设计通常计算量较大或精度不够。本文提出了一种基于支持向量机（Support Vector Machine）和MPP（Most Probable Point）的可靠性分析方法。用SVM 在MPP处替代原极限状态函数，并利用极限状态函数的梯度信息，使SVM模型穿过M PP并与原函数相切，再基于SVM采用重要抽样法计算失效概率。然后，将SORA（Sequential Optimization and Reliability Assessment ）与基于SVM 的可靠性分析方法相集成，将传统的双循环可靠性优化算法解耦为单循环，并通过基于SVM 的可靠性分析方法修正了SORA中由于线性近似带来的误差，保证了最优设计点处可靠性分析的精度。算例证明，该方法在处理非线性问题时具有精确度高和计算量适度的特点。
Differentially Private Support Vector Machines
Sarwate, Anand; Monteleoni, Claire
2009-01-01
This paper addresses the problem of practical privacy-preserving machine learning: how to detect patterns in massive, real-world databases of sensitive personal information, while maintaining the privacy of individuals. Chaudhuri and Monteleoni (2008) recently provided privacy-preserving techniques for learning linear separators via regularized logistic regression. With the goal of handling large databases that may not be linearly separable, we provide privacy-preserving support vector machine algorithms. We address general challenges left open by past work, such as how to release a kernel classifier without releasing any of the training data, and how to tune algorithm parameters in a privacy-preserving manner. We provide general, efficient algorithms for linear and nonlinear kernel SVMs, which guarantee $\\epsilon$-differential privacy, a very strong privacy definition due to Dwork et al. (2006). We also provide learning generalization guarantees. Empirical evaluations reveal promising performance on real and...
Directory of Open Access Journals (Sweden)
Lei Si
2016-01-01
Full Text Available Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.
Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling
2017-02-01
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R(2)) of the whole training set (training and prediction) was 0.924, and the R(2) of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q(2) value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.
Directory of Open Access Journals (Sweden)
Xiang-ming Gao
2017-01-01
Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Liu, Ze; Xu, Jing
2016-01-01
Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system. PMID:26771615
Directory of Open Access Journals (Sweden)
Chuncai Xiao
2014-12-01
Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.
Energy Technology Data Exchange (ETDEWEB)
Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX
2010-08-25
Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the
Energy Technology Data Exchange (ETDEWEB)
Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX
2010-08-25
Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the
Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian
2016-05-11
In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.
Ghaedi, M; Dashtian, K; Ghaedi, A M; Dehghanian, N
2016-05-11
The aim of this work is the study of the predictive ability of a hybrid model of support vector regression with genetic algorithm optimization (GA-SVR) for the adsorption of malachite green (MG) onto multi-walled carbon nanotubes (MWCNTs). Various factors were investigated by central composite design and optimum conditions was set as: pH 8, 0.018 g MWCNTs, 8 mg L(-1) dye mixed with 50 mL solution thoroughly for 10 min. The Langmuir, Freundlich, Temkin and D-R isothermal models are applied to fitting the experimental data, and the data was well explained by the Langmuir model with a maximum adsorption capacity of 62.11-80.64 mg g(-1) in a short time at 25 °C. Kinetic studies at various adsorbent dosages and the initial MG concentration show that maximum MG removal was achieved within 10 min of the start of every experiment under most conditions. The adsorption obeys the pseudo-second-order rate equation in addition to the intraparticle diffusion model. The optimal parameters (C of 0.2509, σ(2) of 0.1288 and ε of 0.2018) for the SVR model were obtained based on the GA. For the testing data set, MSE values of 0.0034 and the coefficient of determination (R(2)) values of 0.9195 were achieved.
A New Incremental Support Vector Machine Algorithm
Directory of Open Access Journals (Sweden)
Wenjuan Zhao
2012-10-01
Full Text Available Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vector machine. The goal concept is updated by incremental learning. Each training procedure only includes new training data. The time complexity is independent of whole training set. Compared with the other incremental version, the training speed of this approach is improved and the change of hyperplane is reduced.
A Fast Algorithm for Support Vector Clustering
Institute of Scientific and Technical Information of China (English)
吕常魁; 姜澄宇; 王宁生
2004-01-01
Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model[3] , the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed.The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
Vector optimization theory, applications, and extensions
Jahn, Johannes
2011-01-01
This new edition of a key monograph has fresh sections on the work of Edgeworth and Pareto in its presentation in a general setting of the fundamentals and important results of vector optimization. It examines background material, applications and theories.
Deep Support Vector Machines for Regression Problems
Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus
2013-01-01
In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain su
Clustering Categories in Support Vector Machines
DEFF Research Database (Denmark)
Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero
2017-01-01
The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...
Deep Support Vector Machines for Regression Problems
Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus
2013-01-01
In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain
Cascade Support Vector Machines with Dimensionality Reduction
Directory of Open Access Journals (Sweden)
Oliver Kramer
2015-01-01
Full Text Available Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Fernández, Michael; Fernández, Leyden; Abreu, Jose Ignacio; Garriga, Miguel
2008-06-01
Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.
Incremental Support Vector Learning for Ordinal Regression.
Gu, Bin; Sheng, Victor S; Tay, Keng Yeow; Romano, Walter; Li, Shuo
2015-07-01
Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν -support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line ν-SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Variable ordering structures in vector optimization
Eichfelder, Gabriele
2014-01-01
This book provides an introduction to vector optimization with variable ordering structures, i.e., to optimization problems with a vector-valued objective function where the elements in the objective space are compared based on a variable ordering structure: instead of a partial ordering defined by a convex cone, we see a whole family of convex cones, one attached to each element of the objective space. The book starts by presenting several applications that have recently sparked new interest in these optimization problems, and goes on to discuss fundamentals and important results on a wide ra
Robust Pseudo-Hierarchical Support Vector Clustering
DEFF Research Database (Denmark)
Hansen, Michael Sass; Sjöstrand, Karl; Olafsdóttir, Hildur
2007-01-01
Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method...... for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial...
A NEW HYPERSPHERE SUPPORT VECTOR MACHINE ALGORITHM
Institute of Scientific and Technical Information of China (English)
Zhang Xinfeng; Shen Lansun
2006-01-01
The hypersphere support vector machine is a new algorithm in pattern recognition. By studying three kinds ofhypersphere support vector machines, it is found that their solutions are identical and the margin between two classes of samples is zero or is not unique. In this letter, a new kind ofhypersphere support vector machine is proposed. By introducing a parameter n(n＞l), a unique solution of the margin can be obtained.Theoretical analysis and experimental results show that the proposed algorithm can achieve better generalization performance.
THE OPTIMALITY CONDITIONS OF NONCONVEX SET-VALUED VECTOR OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
盛保怀; 刘三阳
2002-01-01
The concepts of α-order Clarke's derivative, α-order Adjacent derivative and α-order G.Bouligand derivative of set-valued mappings are introduced, their properties are studied, with which the Fritz John optimality condition of set-valued vector optimization is established. Finally, under the assumption of pseudoconvexity, the optimality condition is proved to be sufficient.
Aalizadeh, Reza; von der Ohe, Peter C; Thomaidis, Nikolaos S
2017-03-22
According to the European REACH Directive, the acute toxicity towards Daphnia magna should be assessed for any industrial chemical with a market volume of more than 1 t/a. Therefore, it is highly recommended to determine the toxicity at a certain confidence level, either experimentally or by applying reliable prediction models. To this end, a large dataset was compiled, with the experimental acute toxicity values (pLC50) of 1353 compounds in Daphnia magna after 48 h of exposure. A novel quantitative structure-toxicity relationship (QSTR) model was developed, using Ant Colony Optimization (ACO) to select the most relevant set of molecular descriptors, and Support Vector Machine (SVM) to correlate the selected descriptors with the toxicity data. The proposed model showed high performance (QLOO(2) = 0.695, Rfitting(2) = 0.920 and Rtest(2) = 0.831) with low root mean square errors of 0.498 and 0.707 for the training and test set, respectively. It was found that, in addition to hydrophobicity, polarizability and summation of solute-hydrogen bond basicity affected toxicity positively, while minimum atom-type E-state of -OH influenced toxicity values in Daphnia magna inversely. The applicability domain of the proposed model was carefully studied, considering the effect of chemical structure and prediction error in terms of leverage values and standardized residuals. In addition, a new method was proposed to define the chemical space failure for a compound with unknown toxicity to avoid using these prediction results. The resulting ACO-SVM model was successfully applied on an additional evaluation set and the prediction results were found to be very accurate for those compounds that fall inside the defined applicability domain. In fact, compounds commonly found to be difficult to predict, such as quaternary ammonium compounds or organotin compounds were outside the applicability domain, while five representative homologues of LAS (non-ionic surfactants) were, on average
Vector optimization set-valued and variational analysis
Chen, Guang-ya; Yang, Xiaogi
2005-01-01
This book is devoted to vector or multiple criteria approaches in optimization. Topics covered include: vector optimization, vector variational inequalities, vector variational principles, vector minmax inequalities and vector equilibrium problems. In particular, problems with variable ordering relations and set-valued mappings are treated. The nonlinear scalarization method is extensively used throughout the book to deal with various vector-related problems. The results presented are original and should be interesting to researchers and graduates in applied mathematics and operations research
Vector variational inequalities and their relations with vector optimization
Directory of Open Access Journals (Sweden)
Surjeet Kaur Suneja
2014-01-01
Full Text Available In this paper, K- quasiconvex, K- pseudoconvex and other related functions have been introduced in terms of their Clarke subdifferentials, where is an arbitrary closed convex, pointed cone with nonempty interior. The (strict, weakly -pseudomonotonicity, (strict K- naturally quasimonotonicity and K- quasimonotonicity of Clarke subdifferential maps have also been defined. Further, we introduce Minty weak (MVVIP and Stampacchia weak (SVVIP vector variational inequalities over arbitrary cones. Under regularity assumption, we have proved that a weak minimum solution of vector optimization problem (VOP is a solution of (SVVIP and under the condition of K- pseudoconvexity we have obtained the converse for MVVIP (SVVIP. In the end we study the interrelations between these with the help of strict K-naturally quasimonotonicity of Clarke subdifferential map.
A New Algorithm for Generalized Optimal Discriminant Vectors
Institute of Scientific and Technical Information of China (English)
吴小俊; 杨静宇; 王士同; 郭跃飞; 曹奇英
2002-01-01
A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper. This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time. A lot of computation time can be saved because all the generalized optimal sets of discriminant vectors can be obtained simultaneously with the proposed algorithm, while it needs no iterative operations. The proposed algorithm can yield a much higher recognition rate. Furthermore,the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only. These statements are supported by the numerical simulation experiments on facial database of ORL.
GenSVM: a generalized multiclass support vector machine
G.J.J. van den Burg (Gerrit); P.J.F. Groenen (Patrick)
2016-01-01
textabstractTraditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classification boundaries for a K-class proble
Ranking Support Vector Machine with Kernel Approximation.
Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi
2017-01-01
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
Masquerade Detection Using Support Vector Machine
Institute of Scientific and Technical Information of China (English)
YANG Min; WANG Li-na; ZHANG Huan-guo; CHEN Wei
2005-01-01
A new method using support vector data description (SVDD) to distinguish legitimate users from masqueraders based on UNIX user command sequences is proposed. Sliding windows are used to get low detection delay.Experiments demonstrate that the detection effect using en riched sequences is better than that of using truncated sequences. As a SVDD profile is composed of a small amount of support vectors, our SVDD-based method can achieve computation and storage advantage when the detection performance is similar to existing method.
Study on Support Vector Machine Based on 1-Norm
Institute of Scientific and Technical Information of China (English)
PAN Mei-qin; HE Guo-ping; HAN Cong-ying; XUE Xin; SHI You-qun
2006-01-01
The model of optimization problem for Support Vector Machine(SVM) is provided, which based on the definitions of the dual norm and the distance between a point and its projection onto a given plane. The model of improved Support Vector Machine based on 1-norm (1 - SVM) is provided from the optimization problem, yet it is a discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method can select and suppress the problem features more efficiently.Illustrative examples show that the 1 - SVM deal with the linear or nonlinear classification well.
An Improved Support Vector Machines： NNSVM
Institute of Scientific and Technical Information of China (English)
LIHonglian; WANGChunhua; YUANBaozong
2004-01-01
In this paper we propose an improved support vector machine: NNSVM. It first prunes the training set, reserves or deletes a sample according to whether its nearest neighbor has same class label with itself or not,then trains the new set with standard SVM to obtain a classifier. Experimental results show that NNSVM is better than SVM in speed and accuracy of classiflcation.
Weighted Twin Support Vector Machine with Universum
Directory of Open Access Journals (Sweden)
Shuxia Lu
Full Text Available Universum is a new concept proposed recently, which is defined to be the sample that does not belong to any classes concerned. Support Vector Machine with Universum (..-SVM is a new algorithm, which can exploit Universum samples to improve the classifica ...
Efficient Multiplicative Updates for Support Vector Machines
DEFF Research Database (Denmark)
Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten
2009-01-01
The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...
Efficient Multiplicative Updates for Support Vector Machines
DEFF Research Database (Denmark)
Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten
2009-01-01
The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...
Estimation of sand liquefaction based on support vector machines
Institute of Scientific and Technical Information of China (English)
苏永华; 马宁; 胡检; 杨小礼
2008-01-01
The origin and influence factors of sand liquefaction were analyzed, and the relation between liquefaction and its influence factors was founded. A model based on support vector machines (SVM) was established whose input parameters were selected as following influence factors of sand liquefaction: magnitude (M), the value of SPT, effective pressure of superstratum, the content of clay and the average of grain diameter. Sand was divided into two classes: liquefaction and non-liquefaction, and the class label was treated as output parameter of the model. Then the model was used to estimate sand samples, 20 support vectors and 17 borderline support vectors were gotten, then the parameters were optimized, 14 support vectors and 6 borderline support vectors were gotten, and the prediction precision reaches 100%. In order to verify the generalization of the SVM method, two other practical samples’ data from two cities, Tangshan of Hebei province and Sanshui of Guangdong province, were dealt with by another more intricate model for polytomies, which also considered some influence factors of sand liquefaction as the input parameters and divided sand into four liquefaction grades: serious liquefaction, medium liquefaction, slight liquefaction and non-liquefaction as the output parameters. The simulation results show that the latter model has a very high precision, and using SVM model to estimate sand liquefaction is completely feasible.
Institute of Scientific and Technical Information of China (English)
吴国洋
2013-01-01
In order to carry out effective identification of bearing running fault,a new method of bearing fault identification was proposed based on characteristic entropy and optimized support vector machine.The empirical mode decomposition method was used to extract the signal energy entropy.Because the redundant information among these entropy values still exist and very serious,the principal component analysis was selected to conduct the reduction of these entropy information and extract the most effective characteristic information as the input to the support vector machine model By means of optimization of particle swarm,the optimal decision making tree was chosen,the classification model of optimal support vector machine was constructed,and state identification and judgement were performed,so that the classification accuracy was improved.A rolling bearing example was given to illustrate the effectness and accuracy of the method.%为了对轴承的故障进行有效的识别,提出基于特征熵和优化支持向量机的轴承故障识别新方法.利用EMD分解信号提取分解信号的能量熵,由于这些熵值之间冗余信息较为严重,因此选用主成分分析对这些熵信息进行约简,提取最有效的特征信息,作为支持向量机模型的输入.通过粒子群优化选取最优决策树构造最佳的支持向量机分类模型进行状态的识别和判定,提高了分类的精确度.通过一个滚动轴承的实例说明方法的有效性和准确性.
Institute of Scientific and Technical Information of China (English)
石志标; 苗莹
2014-01-01
为解决支持向量机算法（Support Vector Machine，SVM）的核函数参数及惩罚因子参数选取的盲目性，利用果蝇优化算法（Fruit Fly Optimization Algorithm，FOA）对 SVM中参数进行优化。提出基于 FOA 的 SVM故障诊断算法，并对汽轮机故障实验数据进行模式识别。该算法能对 SVM相关参数自动寻优，且能达到较理想的全局最优解。通过与常用的粒子群算法（Particle Swarm Optimization，PSO）与遗传算法（Genetic Algorithm，GA）优化后支持向量机进行对比。结果表明，FOA -SVM算法稳定、识别速度快、识别率高。%In order to solve the problem that the selection of the kernel function parameters and penalty factor parameters in the support vector machine(SVM)algorithm is blindfold,the fruit fly optimization algorithm (FOA)was applied to optimize the parameters in SVM.A fault diagnosis algorithm of SVM based on FOA was put forward,and then the pattern recognition of experimental turbine failure data was performed.The algorithm can optimize the SVMparameters automatically,and achieve ideal global optimal solution.Comparing with the SVMwhich is optimized by the commonly used methods of the particle swarm optimization(PSO)and the Genetic Algorithm (GA),the results demonstrate that FOA-SVMhas the fastest recognition speed and the highest recognition rate.
Quantum support vector machine for big data classification.
Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth
2014-09-26
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.
Online support vector regression for reinforcement learning
Institute of Scientific and Technical Information of China (English)
Yu Zhenhua; Cai Yuanli
2007-01-01
The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online support vector regression (OSVR) is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of support vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable convergence speed and can solve continuous problems that are infeasible using lookup table.
Support vector machine for automatic pain recognition
Monwar, Md Maruf; Rezaei, Siamak
2009-02-01
Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.
Computerized Interactive Gaming via Supporting Vector Machines
Jiang, Yang; Jiang, Jianmin; Palmer, Ian
2008-01-01
Computerized interactive gaming requires automatic processing of large volume of random data produced by players on spot, such as shooting, football kicking, and boxing. This paper describes a supporting vector machine-based artificial intelligence algorithm as one of the possible solutions to the problem of random data processing and the provision of interactive indication for further actions. In comparison with existing techniques, such as rule-based and neural networks, and so forth, our S...
Image Segmentation Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
XU Hai-xiang; ZHU Guang-xi; TIAN Jin-wen; ZHANG Xiang; PENG Fu-yuan
2005-01-01
Image segmentation is a necessary step in image analysis. Support vector machine (SVM) approach is proposed to segment images and its segmentation performance is evaluated.Experimental results show that: the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron (BP-MLP) approach and fuzzy c-means (FCM) approach.
Digital VLSI algorithms and architectures for support vector machines.
Anguita, D; Boni, A; Ridella, S
2000-06-01
In this paper, we propose some very simple algorithms and architectures for a digital VLSI implementation of Support Vector Machines. We discuss the main aspects concerning the realization of the learning phase of SVMs, with special attention on the effects of fixed-point math for computing and storing the parameters of the network. Some experiments on two classification problems are described that show the efficiency of the proposed methods in reaching optimal solutions with reasonable hardware requirements.
Investigation of Optimal Integrated Circuit Raster Image Vectorization Method
Directory of Open Access Journals (Sweden)
Leonas Jasevičius
2011-03-01
Full Text Available Visual analysis of integrated circuit layer requires raster image vectorization stage to extract layer topology data to CAD tools. In this paper vectorization problems of raster IC layer images are presented. Various line extraction from raster images algorithms and their properties are discussed. Optimal raster image vectorization method was developed which allows utilization of common vectorization algorithms to achieve the best possible extracted vector data match with perfect manual vectorization results. To develop the optimal method, vectorized data quality dependence on initial raster image skeleton filter selection was assessed.Article in Lithuanian
On First Order Optimality Conditions for Vector Optimization
Institute of Scientific and Technical Information of China (English)
L.M. Gra(n)a Drummond; A.N. Iusem; B.F. Svaiter
2003-01-01
We develop first order optimality conditions for constrained vector optimization. The partial orders for the objective and the constraints are induced by closed and convex cones with nonempty interior.After presenting some well known existence results for these problems, based on a scalarization approach, we establish necessity of the optimality conditions under a Slater-like constraint qualification, and then sufficiency for the K-convex case. We present two alternative sets of optimality conditions, with the same properties in connection with necessity and sufficiency, but which are different with respect to the dimension of the spaces to which the dual multipliers belong. We introduce a duality scheme, with a point-to-set dual objective, for which strong duality holds. Some examples and open problems for future research are also presented.
Twin support vector machines models, extensions and applications
Jayadeva; Chandra, Suresh
2017-01-01
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
Institute of Scientific and Technical Information of China (English)
张浩然; 韩正之; 李昌刚
2002-01-01
This paper gives a introduction of the basic ideas, basic theory, key techniques, and application of the sup-port vector machine (SVM), and indicates the similarities and differences between support vector machines and neuralnetworks.
Perron vector optimization applied to search engines
Fercoq, Olivier
2011-01-01
In the last years, Google's PageRank optimization problems have been extensively studied. In that case, the ranking is given by the invariant measure of a stochastic matrix. In this paper, we consider the more general situation in which the ranking is determined by the Perron eigenvector of a nonnegative, but not necessarily stochastic, matrix, in order to cover Kleinberg's HITS algorithm. We also give some results for Tomlin's HOTS algorithm. The problem consists then in finding an optimal outlink strategy subject to design constraints and for a given search engine. We study the relaxed versions of these problems, which means that we should accept weighted hyperlinks. We provide an efficient algorithm for the computation of the matrix of partial derivatives of the criterion, that uses the low rank property of this matrix. We give a scalable algorithm that couples gradient and power iterations and gives a local minimum of the Perron vector optimization problem. We prove convergence by considering it as an app...
SUPPORT VECTOR MACHINE METHOD FOR PREDICTING INVESTMENT MEASURES
Directory of Open Access Journals (Sweden)
Olga V. Kitova
2016-01-01
Full Text Available Possibilities of applying intelligent machine learning technique based on support vectors for predicting investment measures are considered in the article. The base features of support vector method over traditional econometric techniques for improving the forecast quality are described. Computer modeling results in terms of tuning support vector machine models developed with programming language Python for predicting some investment measures are shown.
A Novel Support Vector Machine with Globality-Locality Preserving
Directory of Open Access Journals (Sweden)
Cheng-Long Ma
2014-01-01
Full Text Available Support vector machine (SVM is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM, is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.
Novel algorithm for constructing support vector machine regression ensemble
Institute of Scientific and Technical Information of China (English)
Li Bo; Li Xinjun; Zhao Zhiyan
2006-01-01
A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine
Directory of Open Access Journals (Sweden)
Wahyudi Setiawan
2014-02-01
Full Text Available Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA is used for feature extraction. Support Vector Machine (SVM is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% . Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR
Vector optimization and monotone operators via convex duality recent advances
Grad, Sorin-Mihai
2014-01-01
This book investigates several duality approaches for vector optimization problems, while also comparing them. Special attention is paid to duality for linear vector optimization problems, for which a vector dual that avoids the shortcomings of the classical ones is proposed. Moreover, the book addresses different efficiency concepts for vector optimization problems. Among the problems that appear when the framework is generalized by considering set-valued functions, an increasing interest is generated by those involving monotone operators, especially now that new methods for approaching them by means of convex analysis have been developed. Following this path, the book provides several results on different properties of sums of monotone operators.
Supernova Recognition using Support Vector Machines
Energy Technology Data Exchange (ETDEWEB)
Romano, Raquel A.; Aragon, Cecilia R.; Ding, Chris
2006-10-01
We introduce a novel application of Support Vector Machines(SVMs) to the problem of identifying potential supernovae usingphotometric and geometric features computed from astronomical imagery.The challenges of this supervised learning application are significant:1) noisy and corrupt imagery resulting in high levels of featureuncertainty,2) features with heavy-tailed, peaked distributions,3)extremely imbalanced and overlapping positiveand negative data sets, and4) the need to reach high positive classification rates, i.e. to find allpotential supernovae, while reducing the burdensome workload of manuallyexamining false positives. High accuracy is achieved viaasign-preserving, shifted log transform applied to features with peaked,heavy-tailed distributions. The imbalanced data problem is handled byoversampling positive examples,selectively sampling misclassifiednegative examples,and iteratively training multiple SVMs for improvedsupernovarecognition on unseen test data. We present crossvalidationresults and demonstrate the impact on a largescale supernova survey thatcurrently uses the SVM decision value to rank-order 600,000 potentialsupernovae each night.
Support vector machines with a reject option
Wegkamp, Marten; 10.3150/10-BEJ320
2012-01-01
This paper studies $\\ell_1$ regularization with high-dimensional features for support vector machines with a built-in reject option (meaning that the decision of classifying an observation can be withheld at a cost lower than that of misclassification). The procedure can be conveniently implemented as a linear program and computed using standard software. We prove that the minimizer of the penalized population risk favors sparse solutions and show that the behavior of the empirical risk minimizer mimics that of the population risk minimizer. We also introduce a notion of classification complexity and prove that our minimizers adapt to the unknown complexity. Using a novel oracle inequality for the excess risk, we identify situations where fast rates of convergence occur.
Support Vector Machines and Generalisation in HEP
Bethani, A.; Bevan, A. J.; Hays, J.; Stevenson, T. J.
2016-10-01
We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.
Mechanical Fault Diagnosis Using Support Vector Machine
Institute of Scientific and Technical Information of China (English)
LI Ling-jun; ZHANG Zhou-suo; HE Zheng-jia
2003-01-01
The Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory ( SLT) , which can get good classification effects even with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearings is conducted. The vibration signals acquired from the bearings are used directly in the calculating without the preprocessing of extracting its features. Compared with the methods based on Artificial Neural Network (ANN), the SVM-based meth-od has desirable advantages. It is applicable for on-line diagnosis of mechanical systems.
Color Image Classification Using Support Vector Machines
Institute of Scientific and Technical Information of China (English)
冯霞
2003-01-01
An efficient method using various histogram-based (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories is presented. Principal component analysis(PCA) is used to project the original high dimensional histograms onto their eigenspaees. Lower dimensional eigenfeatures are then used to train support vector machines(SVMs) to classify images into their categories. Experimental results show that even though different descriptors perform differently,they are all highly redundant. It is shown that the dimensionality of all these descriptors,regardless of their performances,can be significantly reduced without affecting classification accuracy, Such scheme would be useful when it is used in an interactive setting for relevant feedback in content-based image retrieval,where low dimensional content descriptors will enable fast online learning and reclassification of results.
Support Vector Machines and Generalisation in HEP
Bethani, A; Hays, J; Stevenson, T J
2016-01-01
We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.
Applications of Support Vector Machines in Astronomy
Zhang, Y.; Zhao, Y.
2014-05-01
We review Support Vector Machines (SVMs) as applied in astronomy. SVMs are mainly used for solving the and regression issues. Take classification for example, selecting of cataclysmic variables from large spectroscopic survey, detecting quasar candidates from multiwavelength photometric data, identification of blue horizontal branch stars from photometric data, classification of galactic spectra, supernova search; for regression problem, photometric redshift estimation of galaxies and quasars, physical parameter measurement (metallicity, gravity, effective temperature) of stars. Comparatively, SVMs show better performance in classification than in regression. Nevertheless, SVMs has its disadvantages, which needs large computation cost on training. Based on this problem, CUDA-Accelerated SVMs is put forward. As for accuracy of SVMs, SVMs combined with other algorithms has further improvement, such as SVM-KNN.
Incremental learning for ν-Support Vector Regression.
Gu, Bin; Sheng, Victor S; Wang, Zhijie; Ho, Derek; Osman, Said; Li, Shuo
2015-07-01
The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts. Copyright © 2015 Elsevier Ltd. All rights reserved.
Scorebox extraction from mobile sports videos using Support Vector Machines
Kim, Wonjun; Park, Jimin; Kim, Changick
2008-08-01
Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.
Support vector classification algorithm based on variable parameter linear programming
Institute of Scientific and Technical Information of China (English)
Xiao Jianhua; Lin Jian
2007-01-01
To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.
Institute of Scientific and Technical Information of China (English)
丁德臣
2011-01-01
目前涉及遗传算法与支持向量机相结合的预测模型中,遗传算法基本上采用的是标准算法.但是在对全局函数的优化中,一般的遗传算法容易陷入局部最优,从而降低遗传算法收敛速度和搜索精度,进而影响财务风险预警模型的精度与速度.基于此,提出了基于混合全局优化正交遗传算法(HOGA)和支持向量机(SVM)的财务风险预警模型(HOGA-SVM),通过使用混合全局优化正交遗传算法连同支持向量机来改进支持向量机进行财务风险预警的效果.结果显示,提出的模型不仅提高了财务风险预警的准确率和速度,而且模型的两类分类错误率(尤其是第一类分类错误率)相对其他模型也有了明显下降.未来的工作可以把模型的应用扩大到多分类的财务风险预警问题中.%Financial risk premonition can exert a significant influence on a company's survival and growth. Many financial risk premonition models generally fall into two categories: the traditional statistics model and the Al model. A hybrid model that incorporates hybrid genetic algorithms and support vector machines ( SVM ) is becoming an important financial risk promotion model.The simple genetic algorithm is frequently used in the hybrid financial risk premonition model. However, convergence rate has a weak performance in the optimization of multi-objective functions. A financial risk premonition model based on the hybrid orthogonal genetic algorithm for global optimization and support vector machine (HOGA-SVM) is proposed to improve the effect of financial risk premonition.HOGA is used to optimize both subset features and SVM parameters simultaneously. The optimization process includes five steps" ( 1 ) encode subset features and SVM parameters. The chromosomes of SVM parameters are encoded as a 16-bit string that consists of 8 bits standing for C and the other 8 bits standing for. The chromosomes of subset features were further encoded as
Quintic spline smooth semi-supervised support vector classification machine
Institute of Scientific and Technical Information of China (English)
Xiaodan Zhang; Jinggai Ma; Aihua Li; Ang Li
2015-01-01
A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classifi-cation. Since the objective function of the model for an unstrained semi-supervised vector machine is not smooth, many fast opti-mization algorithms cannot be applied to solve the model. In order to overcome the difficulty of dealing with non-smooth objective functions, new methods that can solve the semi-supervised vector machine with desired classification accuracy are in great demand. A quintic spline function with three-times differentiability at the ori-gin is constructed by a general three-moment method, which can be used to approximate the symmetric hinge loss function. The approximate accuracy of the quintic spline function is estimated. Moreover, a quintic spline smooth semi-support vector machine is obtained and the convergence accuracy of the smooth model to the non-smooth one is analyzed. Three experiments are performed to test the efficiency of the model. The experimental results show that the new model outperforms other smooth models, in terms of classification performance. Furthermore, the new model is not sensitive to the increasing number of the labeled samples, which means that the new model is more efficient.
The entire regularization path for the support vector domain description
DEFF Research Database (Denmark)
Sjöstrand, Karl; Larsen, Rasmus
2006-01-01
-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This pa- per shows that this property carries over to the support vector domain description. Using our results......The support vector domain description is a one-class classi- fication method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two...
Density Based Support Vector Machines for Classification
Directory of Open Access Journals (Sweden)
Zahra Nazari
2015-04-01
Full Text Available Support Vector Machines (SVM is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used classification algorithm is very sensitive to these outliers and lacks the ability to discard them. Many research results prove this sensitivity which is a weak point for SVM. Different approaches are proposed to reduce the effect of outliers but no method is suitable for all types of data sets. In this paper, the new method of Density Based SVM (DBSVM is introduced. Population Density is the basic concept which is used in this method for both linear and non-linear SVM to detect outliers. Experiments on artificial data sets, real high-dimensional benchmark data sets of Liver disorder and Heart disease, and data sets of new and fatigued banknotes’ acoustic signals can prove the efficiency of this method on noisy data classification and the better generalization that it can provide compared to the standard SVM.
SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS
Institute of Scientific and Technical Information of China (English)
LI Hong-shuang; L(U) Zhen-zhou; YUE Zhu-feng
2006-01-01
Support vector machine (SVM) was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method (FORM) and the Monte Carlo simulation (MCS). As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence,two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method (RSM) and the artificial neural network (ANN), which are widely used to replace the implicit state function for alleviating the computation cost,the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the "curse of dimensionality". The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis (usually finite element analysis), and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.
Face Behavior Recognition Through Support Vector Machines
Directory of Open Access Journals (Sweden)
Haval A. Ahmed
2016-01-01
Full Text Available Communication between computers and humans has grown to be a major field of research. Facial Behavior Recognition through computer algorithms is a motivating and difficult field of research for establishing emotional interactions between humans and computers. Although researchers have suggested numerous methods of emotion recognition within the literature of this field, as yet, these research works have mainly focused on one method for their system output i.e. used one facial database for assessing their works. This may diminish the generalization method and additionally it might shrink the comparability range. A proposed technique for recognizing emotional expressions that are expressed through facial aspects of still images is presented. This technique uses the Support Vector Machines (SVM as a classifier of emotions. Substantive problems are considered such as diversity in facial databases, the samples included in each database, the number of facial expressions experienced an accurate method of extracting facial features, and the variety of structural models. After many experiments and the results of different models being compared, it is determined that this approach produces high recognition rates.
Support Vector Machine for mechanical faults classification
Institute of Scientific and Technical Information of China (English)
JIANG Zhi-qiang; FU Han-guang; LI Ling-jun
2005-01-01
Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary classifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for online diagnosis for mechanical system.
Recursive support vector machines for dimensionality reduction.
Tao, Qing; Chu, Dejun; Wang, Jue
2008-01-01
The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.
Institute of Scientific and Technical Information of China (English)
林菁; 江琳
2012-01-01
针对粒子群算法易陷入局部最优值的缺点，将免疫原理引入粒子群算法中，利用免疫记忆与自我调节机制促使各适应度层次的粒子维持一定浓度，保证群体的多样性，从而避免算法陷入局部最优。随后将这种改进的算法应用于支持向量机参数的选择，并在BreaStCancer等数据集上进行了实验，实验结果表明利用免疫粒子群算法选取支持向量机最优参数，能够提高支持向量机的分类正确率，具有一定的实用性，特别在经济金融应用上前景可观。%To avoid trapping into local optimization of Particle Swarm Optimization (PSO) algorithm, the principle of immune was introduced to improve the PSO algorithm for searching the optimal parameters of support vector machines (SVM).The improved method utilized the function of immune memory and the self adjustment mechanism to maintain the concentration of particles at a certain level in every layer to guarantee the diversity of population. So it avoided the problem of local optimization. The improved algorithm was verified with the Breast Cancer, Ionosphere and German datasets. The results demonstrate that the algorithm can improve the overall performance of SVM classifier and its application in the field of finance will lead to prosperous future.
Directory of Open Access Journals (Sweden)
Ying-Pei Liu
Full Text Available In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC system, we propose an improved auto-disturbance rejection control (ADRC method based on least squares support vector machines (LSSVM in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD and adaptive optimal kernel (AOK time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.
Liu, Ying-Pei; Liang, Hai-Ping; Gao, Zhong-Ke
2015-01-01
In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD) and adaptive optimal kernel (AOK) time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.
A New Fenchel Dual Problem in Vector Optimization
Indian Academy of Sciences (India)
Radu Ioan Boţ; Anca Dumitru; Gert Wanka
2009-04-01
We introduce a new Fenchel dual for vector optimization problems inspired by the form of the Fenchel dual attached to the scalarized primal multiobjective problem. For the vector primal-dual pair we prove weak and strong duality. Furthermore, we recall two other Fenchel-type dual problems introduced in the past in the literature, in the vector case, and make a comparison among all three duals. Moreover, we show that their sets of maximal elements are equal.
Continuity for vector optimization problems with equilibrium constraints
Institute of Scientific and Technical Information of China (English)
WU Yunan
2004-01-01
The concept of vector optimization problems with equilibrium constraints (VOPEC) is introduced. By using the continuity results of the approximate solution set to the equilibrium problem, we obtain the same results of the marginal map and the approximate value in VOPEC (ε) for vector-valued mapping.
Institute of Scientific and Technical Information of China (English)
闫嘉; 田逢春; 何庆华; 冯敬伟; 贾鹏飞; 孙诚; 樊澍
2012-01-01
针对传统的伤口感染诊断方法耗时长,操作复杂等问题,提出了一种基于电子鼻和支持向量机(SVM)的方法进行伤口感染检测,分别检测非感染和三种常见病原菌感染的大白鼠伤口顶空气体,然后利用SVM对实验数据进行识别.同时,鉴于传感器阵列的优化以及SVM参数选择对其分类准确率有重大的影响,提出一种基于粒子群算法(PSO)的传感器阵列和SVM参数同步优化方法.实验结果表明,SVM结合PSO与传统的神经网络以及遗传算法相比,极大提高伤口感染检测的准确率.%In order to solve the time-consuming and complicated operation problem in traditional diagnosis method of wound infection,a new method based on the electronic nose (enose) and support vector machine (SVM) is proposed to detect wound headspace gases of rats nonin-fected and those infected by three types common pathogens respectively. Meanwhile,owing to the strong impact of optimization of sensor array and parameters selection on the classification accuracy of SVM ,an simultaneous optimization method of sensor array and parameters of SVM based on particle swarm optimization (PSO) is presented. The results show that SVM combined with PSO greatly improves the recognition accuracy rate of wound infection, compared with the traditional neural networks and genetic algorithms.
Institute of Scientific and Technical Information of China (English)
崔东文; 金波
2015-01-01
According to the support vector machine ( SVM) learning parameters are difficult to determine, using Drosophila optimization algorithm ( FOA) search SVM learning parameters———the penalty factor and kernel parameter, put forward FOA -SVM prediction model, and construct based on particle swarm optimization ( PSO) algorithm, a genetic optimization ( GA) algorithm to search the SVM for learning parameters of PSO-SVM model and GA-SVM model as a comparison, in Yunnan Province, Dong Lake Station annual runoff prediction for case study. The results show that: the FOA-SVM model prediction accuracy is better than PSO-SVM and GA-SVM models, have higher prediction precision and generalization ability.%针对支持向量机( SVM)学习参数难以确定的不足,利用果蝇优化算法( FOA)搜寻SVM学习参数———惩罚因子和核函数参数,提出FOA-SVM预测模型,并构建基于粒子群优化( PSO)算法、遗传优化( GA)算法搜寻SVM学习参数的PSO-SVM和GA-SVM模型作为对比,以云南省董湖站年径流预测进行实例研究。结果表明,FOA-SVM模型预测精度优于PSO-SVM和GA-SVM模型,具有较高的预测精度和泛化能力。
Clifford support vector machines for classification, regression, and recurrence.
Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy
2010-11-01
This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.
A Novel Kernel for Least Squares Support Vector Machine
Institute of Scientific and Technical Information of China (English)
FENG Wei; ZHAO Yong-ping; DU Zhong-hua; LI De-cai; WANG Li-feng
2012-01-01
Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
Vector Broadcast Channels: Optimal Threshold Selection Problem
Samarasinghe, Tharaka; Evans, Jamie
2011-01-01
Threshold feedback policies are well known and provably rate-wise optimal selective feedback techniques for communication systems requiring partial channel state information (CSI). However, optimal selection of thresholds at mobile users to maximize information theoretic data rates subject to feedback constraints is an open problem. In this paper, we focus on the optimal threshold selection problem, and provide a solution for this problem for finite feedback systems. Rather surprisingly, we show that using the same threshold values at all mobile users is not always a rate-wise optimal feedback strategy, even for a system with identical users experiencing statistically the same channel conditions. By utilizing the theory of majorization, we identify an underlying Schur-concave structure in the rate function and obtain sufficient conditions for a homogenous threshold feedback policy to be optimal. Our results hold for most fading channel models, and we illustrate an application of our results to familiar Raylei...
Reinforced Angle-based Multicategory Support Vector Machines
Zhang, Chong; Liu, Yufeng; Wang, Junhui; Zhu, Hongtu
2015-01-01
The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of k classification functions for a k-class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with k − 1 functions directly. We prove that RAMSVMs can enjoy Fisher consistency. Moreover, we show that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem. Numerical experiments demonstrate that our method is highly competitive in terms of computational speed, as well as classification prediction performance. Supplemental materials for the article are available online. PMID:27891045
Speaker Identification using MFCC-Domain Support Vector Machine
Kamruzzaman, S M; Islam, Md Saiful; Haque, Md Emdadul; 10.3923/ijepe.2007.274.278
2010-01-01
Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several sam...
An Efficient Audio Classification Approach Based on Support Vector Machines
Directory of Open Access Journals (Sweden)
Lhoucine Bahatti
2016-05-01
Full Text Available In order to achieve an audio classification aimed to identify the composer, the use of adequate and relevant features is important to improve performance especially when the classification algorithm is based on support vector machines. As opposed to conventional approaches that often use timbral features based on a time-frequency representation of the musical signal using constant window, this paper deals with a new audio classification method which improves the features extraction according the Constant Q Transform (CQT approach and includes original audio features related to the musical context in which the notes appear. The enhancement done by this work is also lay on the proposal of an optimal features selection procedure which combines filter and wrapper strategies. Experimental results show the accuracy and efficiency of the adopted approach in the binary classification as well as in the multi-class classification.
Estimating Military Aircraft Cost Using Least Squares Support Vector Machines
Institute of Scientific and Technical Information of China (English)
ZHU Jia-yuan; ZHANG Xi-bin; ZHANG Heng-xi; REN Bo
2004-01-01
A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines(LS-SVM),and a military aircraft life-cycle-cost(LCC)intelligent estimation model is proposed based on the improved LS-SVM.The intelligent cost estimation process is divided into three steps in the model.In the first step,a cost-drive-factor needs to be selected,which is significant for cost estimation.In the second step,military aircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM.Then the model can be used for new type aircraft cost estimation.Chinese military aircraft costs are estimated in the paper.The results show that the estimated costs by the new model are closer to the true costs than that of the traditionally used methods.
Institute of Scientific and Technical Information of China (English)
向国齐
2016-01-01
对钛合金材料Ti6Al4V铣削加工进行有限元数值计算，结合试验设计方法构建了基于支持向量回归机（SVR）的铣削力预测模型，以材料去除率和刀具寿命为优化目标，提出一种基于支持向量回归机和带精英策略的非支配排序遗传算法（NSGA-II）的优化方法。结果表明，该方法能够获得满意的Pareto解集，为钛合金铣削参数优化提供一种新的方法，具有良好的推广价值。%In this paper, the Titanium Alloy Ti6Al4V milling process is analysized by ifnite element method, a milling force prediction model was established based on Support Vector Regression (SVR), The optimization design methodology based on SVR and NSGA-II is proposed for Titanium Alloy milling process cutting parameters. The results show that this methodology has a good performance in ifnding satisfying Pareto solutions, and thus can be used in the machining process parameters optimum and other material processing ifelds.
Upper Hölder continuity of parametric vector optimization problems
Directory of Open Access Journals (Sweden)
Xian-Fu Hu
2016-11-01
Full Text Available Abstract This paper is concerned with upper Hölder continuity and Hölder calmness of a perturbed vector optimization problem. We establish some new sufficient conditions for upper Hölder continuity and Hölder calmness of the perturbed solution mappings and the perturbed optimal value mappings of a vector optimization problem under the case that the objective function and the feasible set are, respectively, perturbed by parameters. Our results generalize and extend the corresponding ones of Li and Li (Appl. Math. Comput. 232:908-918, 2014.
Institute of Scientific and Technical Information of China (English)
朱帮助; 魏一鸣
2011-01-01
针对国际碳市场价格预测LSSVM建模输入节点和模型参数难以确定的问题,建立了基于数据分组处理方法(GMDH)-粒子群算法(PSO)-最小二乘支持向量机(LSSVM)的国际碳市场价格预测模型.首先利用GMDH算法获得LSSVM建模中的输入变量；其次应用PSO算法对LSSVM建模中的参数进行优化,进而使用训练好的LSSVM模型对测试样本进行预测；最后采用该模型对欧盟排放交易体系(EU ETS)两个不同到期时间的碳期货价格(DEC 10和DEC 12)进行实证分析,取得了令人满意的效果.%Aiming at the problems of determining the inputs and parameters for least squares support vector machines (LSSVM) modeling', this paper presents an integrated model of group method of data handling (GMDH), particle swarm optimization (PSO) and LSSVM, I.e., GMDH-PSO-LSSVM, for international carbon price prediction. First, GMDH is used to make the selection of input-layer units easily. Next, PSO is used to train LSSVM model with the training samples and obtain the optimal parameters. Then, the trained LSSVM is used to forecast carbon price of the testing samples. Finally, taking two carbon futures prices with different maturity called DEC 10 and DEC 12 of European Union emissions trading scheme (EU ETS) as samples, empirical results show that the proposed model is an effective way to improve forecasting accuracy.
Weighted K-means support vector machine for cancer prediction
Kim, Sunghwan
2016-01-01
To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble tec...
Support vector regression model for complex target RCS predicting
Institute of Scientific and Technical Information of China (English)
Wang Gu; Chen Weishi; Miao Jungang
2009-01-01
The electromagnetic scattering computation has developed rapidly for many years; some computing problems for complex and coated targets cannot be solved by using the existing theory and computing models. A computing model based on data is established for making up the insufficiency of theoretic models. Based on the "support vector regression method", which is formulated on the principle of minimizing a structural risk, a data model to predicate the unknown radar cross section of some appointed targets is given. Comparison between the actual data and the results of this predicting model based on support vector regression method proved that the support vector regression method is workable and with a comparative precision.
NESVM: a Fast Gradient Method for Support Vector Machines
Zhou, Tianyi; Wu, Xindong
2010-01-01
Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf \\cite{SVM_Perf}\\cite{PerfML} (its convergence rate in solving the dual SVM is upper bounded by $\\mathcal O(1/\\sqrt{k})$, wherein $k$ is the number of iterations.) and Pegasos \\cite{Pegasos} (online SVM that converges at rate $\\mathcal O(1/k)$ for the primal SVM), NESVM achieves the optimal convergence rate at $\\mathcal O(1/k^{2})$ and a linear time complexity. In particular, NESVM smoothes the non-differentiable hinge loss and $\\ell_1$-norm in the primal SVM. Then the optimal gradient method without any line search is ado...
Fuzzy rule-based support vector regression system
Institute of Scientific and Technical Information of China (English)
Ling WANG; Zhichun MU; Hui GUO
2005-01-01
In this paper,we design a fuzzy rule-based support vector regression system.The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from the training data set.Based on the first-order linear Tagaki-Sugeno (TS) model,the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method.Our model is applied to the real world regression task.The simulation results gives promising performances in terms of a set of fuzzy rules,which can be easily interpreted by humans.
DNA regulatory motif selection based on support vector machine ...
African Journals Online (AJOL)
DNA regulatory motif selection based on support vector machine (SVM) and its application in microarray ... African Journal of Biotechnology ... experiments to explore the underlying relationships between motif types and gene functions.
An Introduction to Support Vector Machines: A Review
Chen, Yiling; Councill, Isaac G.
2003-01-01
Review of "An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Nello Cristianini and John Shawe-Taylor, New York, Cambridge University Press, 2000, 189 pp., $45, ISBN 0-521-78019-5.
A support vector machine approach to the development of an ...
African Journals Online (AJOL)
PROMOTING ACCESS TO AFRICAN RESEARCH ... Abstract. This paper demonstrated the use of support vector machine (SVM) model to develop an ... system application and implementation was carried out with java programming language.
Bifurcations of optimal vector fields: an overview
Kiseleva, T.; Wagener, F.; Rodellar, J.; Reithmeier, E.
2009-01-01
We develop a bifurcation theory for the solution structure of infinite horizon optimal control problems with one state variable. It turns out that qualitative changes of this structure are connected to local and global bifurcations in the state-costate system. We apply the theory to investigate an
Prediction in Marketing Using the Support Vector Machine
Dapeng Cui; David Curry
2005-01-01
Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction...
Institute of Scientific and Technical Information of China (English)
郭正红; 赵丙辰
2013-01-01
In order to improve the brain CT image classification accuracy, this paper proposes brain CT mage classification mod-el(IHS-LSSVM)based on the least squares support vector machine and harmony search algorithm. Firstly, the LSSVM parame-ters are taken as different musical tone combination, and then the harmony search algorithm is used to find the optimal parame-ters, and the optimal position adjustment strategy is introduced to enhance the ability of jumping out of local minima, the brain CT image classification model is established according to the optimal parameters, and the performance of the model is tested. The simulation results show that, compared with the other models, IHS-LSSVM not only improves the image classification accu-racy, but also accelerates the classification speed, so it is an effective brain CT image classification model.%为了提高脑CT图像的分类正确率，针对分类器中的最小二乘支持向量机（LSSVM）参数优化问题，提出一种改进和声搜索算法优化LSSVM的脑CT图像分类模型（IHS-LSSVM）。将LSSVM参数看作不同乐器的声调组合，通过和声搜索算法的“调音”找到最优参数，并在寻优过程中引入粒子群算法的最优位置更新策略，增强了算法跳出局部极小值的能力，根据最优参数建立脑CT图像分类模型，并对模型的性能进行仿真测试。仿真结果表明，相对于对比模型，IHS-LSSVM不仅提高了脑CT图像分类正确率，而且加快分类速度，是一种有效的脑CT图像分类模型。
MULTI SUPPORT VECTOR MACHINES DECISION MODEL AND ITS APPLICATION
Institute of Scientific and Technical Information of China (English)
阎威武; 陈治纲; 邵惠鹤
2002-01-01
Support Vector Machines (SVM) is a powerful machine learning method developed from statistical learning theory and is currently an active field in artificial intelligent technology. SVM is sensitive to noise vectors near hyperplane since it is determined only by few support vectors. In this paper, Multi SVM decision model(MSDM)was proposed. MSDM consists of multiple SVMs and makes decision by synthetic information based on multi SVMs. MSDM is applied to heart disease diagnoses based on UCI benchmark data set. MSDM somewhat inproves the robust of decision system.
Fast and Accurate Support Vector Machines on Large Scale Systems
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Vishnu, Abhinav; Narasimhan, Jayenthi; Holder, Larry; Kerbyson, Darren J.; Hoisie, Adolfy
2015-09-08
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.
Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.
Sun, Shiliang; Xie, Xijiong
2016-09-01
Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.
Necessary Optimality Conditions for a Class of Nonsmooth Vector Optimization
Institute of Scientific and Technical Information of China (English)
Hui-xian Wu; He-zhi Luo
2009-01-01
The Kuhn-Tucker type necessary conditions of weak efficiency are given for the problem of mini-mizing a vector function whose each component is the sum of a differentiable function and a convex function,subject to a set of differentiable nonlinear inequalities on a convex subset C of Rn,under the conditions similar to the Abadie constraint qualification,or the Kuhn-Tucker constraint qualification,or the Arrow-Hurwicz-Uzawa constraint qualification.
Improved Support Vector Machine Approach Based on Determining Thresholds Automatically
Institute of Scientific and Technical Information of China (English)
WANG Xiao-hua; YAN Xue-mei; WANG Xiao-guang
2007-01-01
To improve the training speed of support vector machine (SVM), a method called improved center distance ratio method (ICDRM) with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental results show that the improved SVM method-ICDRM does well at identification rate and training speed.
Image Reconstruction Using Pixel Wise Support Vector Machine SVM Classification.
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Mohammad Mahmudul Alam Mia
2015-02-01
Full Text Available Abstract Image reconstruction using support vector machine SVM has been one of the major parts of image processing. The exactness of a supervised image classification is a function of the training data used in its generation. In this paper we studied support vector machine for classification aspects and reconstructed an image using support vector machine. Firstly value of the random pixels is used as the SVM classifier. Then the SVM classifier is trained by using those values of the random pixels. Finally the image is reconstructed after cross-validation with the trained SVM classifier. Matlab result shows that training with support vector machine produce better results and great computational efficiency with only a few minutes of runtime is necessary for training. Support vector machine have high classification accuracy and much faster convergence. Overall classification accuracy is 99.5. From our experiment It can be seen that classification accuracy mostly depends on the choice of the kernel function and best estimation of parameters for kernel is critical for a given image.
Relationship Between Support Vector Set and Kernel Functions in SVM
Institute of Scientific and Technical Information of China (English)
张铃; 张钹
2002-01-01
Based on a constructive learning approach, covering algorithms, we investigatethe relationship between support vector sets and kernel functions in support vector machines(SVM). An interesting result is obtained. That is, in the linearly non-separable case, any sampleof a given sample set K can become a support vector under a certain kernel function. The resultshows that when the sample set K is linearly non-separable, although the chosen kernel functionsatisfies Mercer's condition its corresponding support vector set is not necessarily the subsetof K that plays a crucial role in classifying K. For a given sample set, what is the subsetthat plays the crucial role in classification? In order to explore the problem, a new concept,boundary or boundary points, is defined and its properties are discussed. Given a sample setK, we show that the decision functions for classifying the boundary points of K are the sameas that for classifying the K itself. And the boundary points of K only depend on K and thestructure of the space at which K is located and independent of the chosen approach for findingthe boundary. Therefore, the boundary point set may become the subset of K that plays acrucial role in classification. These results are of importance to understand the principle of thesupport vector machine (SVM) and to develop new learning algorithms.
Institute of Scientific and Technical Information of China (English)
焦有权; 赵礼曦; 邓欧; 徐伟恒; 冯仲科
2013-01-01
several sections, and each section’s volume were summed up as the total tree volume. Based the analytic data, the unary models between diameter at breast and volume were established, and also, to set diameter at breast and tree height as independent variables, tree volume as dependent variable, the binary models could be established, as well as a ternary model that describes the relationship between volume and 3 independent variables including diameter at breast, tree height, and tree step form. Nevertheless, these models mentioned above are sample linear models or nonlinear models. To estimate the forest stocks in the forest survey, former researchers usually cut down target trees and extracted samples based on the principle of sampling, and then made a corresponding volume table. This felled, destructive, and time-consuming method damaged many growth dominant trees. Tree volume modeling is the key step of volume table establishment, and volume usually was predicted by the volume equation that was derived from experience. However, because of the uncertainty of tree growth, it is difficult to effectively predict the complexity and diversity of the volume model through conventional volume equations. For this reason, the volume prediction accuracy rate is unsatisfactory. In order to promote the volume prediction accuracy rate, the algorithm of particle swarm optimization (PSO) was introduced into the standing tree volume prediction model. Moreover, the parameters were optimized by the support vector regression (SVM). The data of diameters at breast height and tree heights of standing trees were input into SVM, which were used to learn, parameters of SVM were used as the particle of PSO, standing trees volume value that were measured by authors were considered as objective function of PSO, then prediction values of standing trees volume were detected by the optimized parameters which were obtained through mutual co-ordination of particle, and the prediction values of
Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, A. M.; Gascón-Moreno, J.; Portilla-Figueras, A.
In this paper we present the application of a support vector regression algorithm to a real problem of maximum daily tropospheric ozone forecast. The support vector regression approach proposed is hybridized with an heuristic for optimal selection of hyper-parameters. The prediction of maximum daily ozone is carried out in all the station of the air quality monitoring network of Madrid. In the paper we analyze how the ozone prediction depends on meteorological variables such as solar radiation and temperature, and also we perform a comparison against the results obtained using a multi-layer perceptron neural network in the same prediction problem.
Incremental Support Vector Machine Framework for Visual Sensor Networks
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Yuichi Motai
2007-01-01
Full Text Available Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.
Detection of Splice Sites Using Support Vector Machine
Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika
Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.
A Reformulation of Support Vector Machines for General Confidence Functions
Guo, Yuhong; Schuurmans, Dale
We present a generalized view of support vector machines that does not rely on a Euclidean geometric interpretation nor even positive semidefinite kernels. We base our development instead on the confidence matrix—the matrix normally determined by the direct (Hadamard) product of the kernel matrix with the label outer-product matrix. It turns out that alternative forms of confidence matrices are possible, and indeed useful. By focusing on the confidence matrix instead of the underlying kernel, we can derive an intuitive principle for optimizing example weights to yield robust classifiers. Our principle initially recovers the standard quadratic SVM training criterion, which is only convex for kernel-derived confidence measures. However, given our generalized view, we are then able to derive a principled relaxation of the SVM criterion that yields a convex upper bound. This relaxation is always convex and can be solved with a linear program. Our new training procedure obtains similar generalization performance to standard SVMs on kernel-derived confidence functions, but achieves even better results with indefinite confidence functions.
Institute of Scientific and Technical Information of China (English)
赵艳南; 牛瑞卿; 彭令; 程温鸣
2015-01-01
以三峡库区白水河滑坡为例，首先分析降雨量与库水位等影响因素与滑坡变形特征的响应关系，然后利用粗糙集理论对10个初始影响因子进行属性约减，筛选出影响滑坡变形的核因子集，最后基于该因子集建立粒子群优化支持向量回归模型，对滑坡位移速率进行预测。研究结果表明：测试样本的预测结果与实测值变化趋势基本一致，其平均绝对误差为0.234 mm/d，均方差和判定系数分别为0.163和0.520。粗糙集理论在分析滑坡变形特征、筛选关键因子方面的适用性与科学性，构建的粗糙集−粒子群优化支持向量机模型具有较高的泛化能力，是一种有效的滑坡变形预测方法。%The Baishuihe landslide in the Three Gorges Reservoir region was selected as an example. By analysing the response relationships between landslide deformation and influencing factors such as the rainfall and the reservoir water level, 10 initial influencing factors were reduced by using the rough set theory(RS). Then, the nuclear factor set influencing the landslide deformation was screened out. Finally, the particle swarm optimization (PSO)− support vector regression (SVR) model was established based on the nuclear factor set to predict landslide displacement rate. The results show that the test sample predictive mean absolute error, mean squared error and determination coefficient are 0.234 mm/d, 0.163 and 0.520, respectively. And the change trends are consistent between predicted results and the measured ones. The rough set theory is scientific and applicable in analysing landslide deformation characteristics and selecting key factors. The RS-PSO-SVR model is an effective method in landslide deformation predicting with high generalization ability.
Image denoising using least squares wavelet support vector machines
Institute of Scientific and Technical Information of China (English)
Guoping Zeng; Ruizhen Zhao
2007-01-01
We propose a new method for image denoising combining wavelet transform and support vector machines (SVMs). A new image filter operator based on the least squares wavelet support vector machines (LSWSVMs) is presented. Noisy image can be denoised through this filter operator and wavelet thresholding technique. Experimental results show that the proposed method is better than the existing SVM regression with the Gaussian radial basis function (RBF) and polynomial RBF. Meanwhile, it can achieve better performance than other traditional methods such as the average filter and median filter.
An extended Lagrangian support vector machine for classifications
Institute of Scientific and Technical Information of China (English)
YANG Xiaowei; SHU Lei; HAO Zhifeng; LIANG Yanchun; LIU Guirong; HAN Xu
2004-01-01
Lagrangian support vector machine (LSVM) cannot solve large problems for nonlinear kernel classifiers. In order to extend the LSVM to solve very large problems, an extended Lagrangian support vector machine (ELSVM) for classifications based on LSVM and SVMlight is presented in this paper. Our idea for the ELSVM is to divide a large quadratic programming problem into a series of subproblems with small size and to solve them via LSVM. Since the LSVM can solve small and medium problems for nonlinear kernel classifiers, the proposed ELSVM can be used to handle large problems very efficiently. Numerical experiments on different types of problems are performed to demonstrate the high efficiency of the ELSVM.
Classification using least squares support vector machine for reliability analysis
Institute of Scientific and Technical Information of China (English)
Zhi-wei GUO; Guang-chen BAI
2009-01-01
In order to improve the efficiency of the support vector machine (SVM) for classification to deal with a large amount of samples,the least squares support vector machine (LSSVM) for classification methods is introduced into the reliability analysis.To reduce the computational cost,the solution of the SVM is transformed from a quadratic programming to a group of linear equations.The numerical results indicate that the reliability method based on the LSSVM for classification has higher accuracy and requires less computational cost than the SVM method.
WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION
Institute of Scientific and Technical Information of China (English)
Tong Yubing; Yang Dongkai; Zhang Qishan
2006-01-01
Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.
Prediction of Banking Systemic Risk Based on Support Vector Machine
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Shouwei Li
2013-01-01
Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.
Virtual screening with support vector machines and structure kernels
Mahé, Pierre
2007-01-01
Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods molecules do not need to be represented and stored explicitly as vectors or fingerprints, but only to be compared to each other through a comparison function technically called a kernel. While classical kernels can be used to compare vector or fingerprint representations of molecules, completely new kernels were developed in the recent years to directly compare the 2D or 3D structures of molecules, without the need for an explicit vectorization step through the extraction of molecular descriptors. While still in their infancy, these approaches have already demonstrated their relevance on several toxicity prediction and s...
Profiled support vector machines for antisense oligonucleotide efficacy prediction
Directory of Open Access Journals (Sweden)
Martín-Guerrero José D
2004-09-01
Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated...... with the hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long...... horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average...
Small-time scale network traffic prediction based on a local support vector machine regression model
Institute of Scientific and Technical Information of China (English)
Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua
2009-01-01
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
Lithium-ion battery remaining useful life prediction based on grey support vector machines
Directory of Open Access Journals (Sweden)
Xiaogang Li
2015-12-01
Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.
Robust support vector machine-trained fuzzy system.
Forghani, Yahya; Yazdi, Hadi Sadoghi
2014-02-01
Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.
Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.
Berikol, Göksu Bozdereli; Yildiz, Oktay; Özcan, I Türkay
2016-04-01
Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data.
Analog neural network for support vector machine learning.
Perfetti, Renzo; Ricci, Elisa
2006-07-01
An analog neural network for support vector machine learning is proposed, based on a partially dual formulation of the quadratic programming problem. It results in a simpler circuit implementation with respect to existing neural solutions for the same application. The effectiveness of the proposed network is shown through some computer simulations concerning benchmark problems.
Ischemic Segment Detection using the Support Vector Domain Description
DEFF Research Database (Denmark)
Hansen, Michael Sass; Ólafsdóttir, Hildur; Sjöstrand, Karl
2007-01-01
and registration of the myocardium provided pixel-wise signal intensity curves that were analyzed using the Support Vector Domain Description (SVDD). In contrast to normal SVDD, the entire regularization path was calculated and used to calculate a generalized distance. The results corresponded well to the ischemic...
Estimate of error bounds in the improved support vector regression
Institute of Scientific and Technical Information of China (English)
SUN Yanfeng; LIANG Yanchun; WU Chunguo; YANG Xiaowei; LEE Heow Pueh; LIN Wu Zhong
2004-01-01
An estimate of a generalization error bound of the improved support vector regression(SVR)is provided based on our previous work.The boundedness of the error of the improved SVR is proved when the algorithm is applied to the function approximation.
Prediction of Machine Tool Condition Using Support Vector Machine
Wang, Peigong; Meng, Qingfeng; Zhao, Jian; Li, Junjie; Wang, Xiufeng
2011-07-01
Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.
Predicting post-translational lysine acetylation using support vector machines
DEFF Research Database (Denmark)
Gnad, Florian; Ren, Shubin; Choudhary, Chunaram
2010-01-01
spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico...
Support Vector Machine-Based Nonlinear System Modeling and Control
Institute of Scientific and Technical Information of China (English)
张浩然; 韩正之; 冯瑞; 于志强
2003-01-01
This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM.At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.
Evaluating automatically parallelized versions of the support vector machine
Codreanu, Valeriu; Droge, Bob; Williams, David; Yasar, Burhan; Yang, Fo; Liu, Baoquan; Dong, Feng; Surinta, Olarik; Schomaker, Lambertus; Roerdink, Jos; Wiering, Marco
2014-01-01
The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the
Data fusion for fault diagnosis using multi-class Support Vector Machines
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space.Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.
Institute of Scientific and Technical Information of China (English)
陈道君; 龚庆武; 金朝意; 张静; 王定美
2013-01-01
智能电网的建设和大规模风电接入电网对短期风电功率预测精度提出了更高的要求。为了克服支持向量回归机(support vector regression machine，SVR)依赖人为经验选择学习参数的弊端，在量子粒子群优化(quantum-behaved particle swarm optimization，QPSO)算法中加入自适应早熟判定准则、混合扰动算子和动态扩张−收缩系数，提出了自适应扰动量子粒子群优化算法(adaptive disturbance quantum-behaved particle swarm optimization，ADQPSO)，并使用ADQPSO 优化选择SVR 的学习参数。实例研究表明， ADQPSO 算法全局寻优能力强、鲁棒性好、计算耗时短，利用ADQPSO 优化得到的SVR 参数，可有效提高模型的预测精度；与反向传播神经网络(back propagation neural network，BPNN)和径向基神经网络(radial basis function neural network，RBFNN)相比，提出的ADQPSO-SVR 能够提高短期风电功率预测的准确性和稳定性。%A higher accuracy of short-term wind farm output prediction is required due to the construction of smart grid and grid-connection of large-scale wind farms. To remedy the defect of support vector regression machine (SVR) that the learning parameter selection of SVR depends on factitious experiences, adaptive disturbance quantum-behaved particle swarm optimization (ADQPSO) algorithm is proposed by adding adaptive premature criterion, mixed disturbance operator and dynamic expansion-contraction coefficient in quantum-behaved particle swarm optimization (QPSO) algorithm, and ADQPSO algorithm is used in optimized selection of learning parameters for SVR. Case study shows that the proposed ADQPSO algorithm possesses such advantages as good global search ability, strong robustness and high computation efficiency, and applying the ADQPSO algorithm to the optimization of the obtained learning parameters of SVR the accuracy of short-term wind power prediction is higher than those by back propagation
Simultaneous topology optimization of structures and supports
DEFF Research Database (Denmark)
Buhl, Thomas
2002-01-01
The purpose of this paper is to demonstrate a method for and the benefits of simultaneously designing structure and support distribution using topology optimization. The support conditions are included in the topology optimization by introducing, a new set of design variables that represents...... cost of supports in a design domain. Other examples show that more efficient mechanisms are obtained by introducing the support conditions in the topology optimization problem....
Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine
Directory of Open Access Journals (Sweden)
Xiaochen Zhang
2017-01-01
Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.
Compressive MUSIC with optimized partial support for joint sparse recovery
Kim, Jong Min; Ye, Jong Chul
2011-01-01
Multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. The MMV problems had been traditionally addressed either by sensor array signal processing or compressive sensing. However, recent breakthrough in this area such as compressive MUSIC (CS-MUSIC) or subspace-augumented MUSIC (SA-MUSIC) optimally combines the compressive sensing (CS) and array signal processing such that $k-r$ supports are first found by CS and the remaining $r$ supports are determined by generalized MUSIC criterion, where $k$ and $r$ denote the sparsity and the independent snapshots, respectively. Even though such hybrid approach significantly outperforms the conventional algorithms, its performance heavily depends on the correct identification of $k-r$ partial support by compressive sensing step, which often deteriorate the overall performance. The main contribution of this paper is, therefore, to show that as long as $k-r+1$ correct supports are included in any $k$...
Exact Dynamic Support Tracking with Multiple Measurement Vectors using Compressive MUSIC
Kim, Jong Min; Ye, Jong Chul
2011-01-01
Dynamic tracking of sparse targets has been one of the important topics in array signal processing. Recently, compressed sensing (CS) approaches have been extensively investigated as a new tool for this problem using partial support information obtained by exploiting temporal redundancy. However, most of these approaches are formulated under single measurement vector compressed sensing (SMV-CS) framework, where the performance guarantees are only in a probabilistic manner. The main contribution of this paper is to allow \\textit{deterministic} tracking of time varying supports with multiple measurement vectors (MMV) by exploiting multi-sensor diversity. In particular, we show that a novel compressive MUSIC (CS-MUSIC) algorithm with optimized partial support selection not only allows removal of inaccurate portion of previous support estimation but also enables addition of newly emerged part of unknown support. Numerical results confirm the theory.
The Sorting Methods of Support Vector Clustering Based on Boundary Extraction and Category Utility
Directory of Open Access Journals (Sweden)
Chen Weigao
2016-01-01
Full Text Available According to the problems of low accuracy and high computational complexity in the classification of unknown radar signals, a method of unsupervised Support Vector Clustering (SVC based on boundary extraction and Category Utility (CU of unknown radar signals is studied. By analyzing the principle of SVC, only the boundary data of data sets contribute to the support vector extracted. Thus firstly, for reducing the data set, at the same time reducing the computational complexity, the algorithm is designed to extract the boundary data through local normal vector. Then using CU select the optimal parameters. At last distinguish different categories and get the sorting results by Cone Cluster Labelling (CCL and Depth-First Search (DFS. Through comparing the simulation results, the proposed method which is based on boundary extraction and CU is proved to have turned out quite good time effectiveness, which not only improves the accuracy of classification, but also reduces the computational complexity greatly.
Support vector machine classification trees based on fuzzy entropy of classification.
de Boves Harrington, Peter
2017-02-15
The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no gaps in the data space confounding this type of classifier. A novel algorithm was devised that uses fuzzy entropy to find optimal partitions for situations when clusters of data are overlapped in the data space. Also, a kernel version of the fuzzy entropy algorithm was devised. A fast support vector machine implementation is used that has no cost C or slack variables to optimize. Statistical comparisons using bootstrapped Latin partitions among the tree classifiers were made using a synthetic XOR data set and validated with ten prediction sets comprised of 50,000 objects and a data set of NMR spectra obtained from 12 tea sample extracts.
A one-layer recurrent neural network for support vector machine learning.
Xia, Youshen; Wang, Jun
2004-04-01
This paper presents a one-layer recurrent neural network for support vector machine (SVM) learning in pattern classification and regression. The SVM learning problem is first converted into an equivalent formulation, and then a one-layer recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification and regression. Compared with the existing two-layer neural network for the SVM classification, the proposed neural network has a low complexity for implementation. Moreover, the proposed neural network can converge exponentially to the optimal solution of SVM learning. The rate of the exponential convergence can be made arbitrarily high by simply turning up a scaling parameter. Simulation examples based on benchmark problems are discussed to show the good performance of the proposed neural network for SVM learning.
Adaptive support vector regression for UAV flight control.
Shin, Jongho; Jin Kim, H; Kim, Youdan
2011-01-01
This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.
Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering
Institute of Scientific and Technical Information of China (English)
Yuan Ping; Ying-Jie Tian; Ya-Jian Zhou; Yi-Xian Yang
2012-01-01
Support vector clustering (SVC) is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes. However,SVC's popularity is degraded by its highly intensive time complexity and poor label performance.To overcome such problems,we present a novel efficient and robust convex decomposition based cluster labeling (CDCL) method based on the topological property of dataset.The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors (SVs).According to a robust algorithm applied in the nearest neighboring convex hulls,the adjacency matrix of convex hulls is built up for finding the connected components; and the remaining data points would be assigned the label of the nearest convex hull appropriately.The approach's validation is guaranteed by geometric proofs.Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.
Support vector machine-based multi-model predictive control
Institute of Scientific and Technical Information of China (English)
Zhejing BA; Youxian SUN
2008-01-01
In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.
Monitoring Grinding Wheel Redress-life Using Support Vector Machines
Institute of Scientific and Technical Information of China (English)
Xun Chen; Thitikorn Limchimchol
2006-01-01
Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machines in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform (STFT) to identify features for condition monitoring. The Support Vector Machine (SVM) methodology is introduced as a powerful tool for the classification of different wheel wear situations.After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.
Evolutionary Support Vector Machines for Transient Stability Monitoring
Dora Arul Selvi, B.; Kamaraj, N.
2012-03-01
Currently, power systems are in the need of fast and reliable contingency monitoring systems for the purpose of maintaining stability in the presence of deregulated and open market environment. In this paper, a quick and unfailing transient stability monitoring algorithm that considers both the symmetrical and unsymmetrical faults is presented. support vector machines (SVMs) are employed as pattern classifiers so as to construct fast relation mappings between the transient stability results and the selected input attributes using mutual information. The type of fault is recognized by a SVM classifier and the critical clearing time of the fault is estimated by a support vector regression machine. The SVM parameters are tuned by an elitist multi-objective non-dominated sorting genetic algorithm in such a manner that the best classification and regression performance are accomplished. To demonstrate the good potential of the scheme, IEEE 3 generator system and a South Indian Grid are utilized.
Fault Isolation for Nonlinear Systems Using Flexible Support Vector Regression
Directory of Open Access Journals (Sweden)
Yufang Liu
2014-01-01
Full Text Available While support vector regression is widely used as both a function approximating tool and a residual generator for nonlinear system fault isolation, a drawback for this method is the freedom in selecting model parameters. Moreover, for samples with discordant distributing complexities, the selection of reasonable parameters is even impossible. To alleviate this problem we introduce the method of flexible support vector regression (F-SVR, which is especially suited for modelling complicated sample distributions, as it is free from parameters selection. Reasonable parameters for F-SVR are automatically generated given a sample distribution. Lastly, we apply this method in the analysis of the fault isolation of high frequency power supplies, where satisfactory results have been obtained.
Estimating coal reserves using a support vector machine
Institute of Scientific and Technical Information of China (English)
LIU Wen-kai; WANG Rui-fang; ZHENG Xiao-juan
2008-01-01
The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau's as the input data. Then coal reserves within a particular region were calculated. These cal-culated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.
Saudi License Plate Recognition Algorithm Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
Khaled Suwais; Rana Al-Otaibi; Ali Alshahrani
2013-01-01
License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.
Support vector regression-based internal model control
Institute of Scientific and Technical Information of China (English)
HUANG Yan-wei; PENG Tie-gen
2007-01-01
This paper proposes a design of internal model control systems for process with delay by using support vector regression (SVR). The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle. Closed-system stability and steady error are analyzed for the existence of modeling errors. The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.
Chord Recognition Based on Temporal Correlation Support Vector Machine
Zhongyang Rao; Xin Guan; Jianfu Teng
2016-01-01
In this paper, we propose a method called temporal correlation support vector machine (TCSVM) for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and...
Inverse Learning Control of Nonlinear Systems Using Support Vector Machines
Institute of Scientific and Technical Information of China (English)
HU Zhong-hui; LI Yuan-gui; CAI Yun-ze; XU Xiao-ming
2005-01-01
An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverselearning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.
Support vector based battery state of charge estimator
Hansen, Terry; Wang, Chia-Jiu
This paper investigates the use of a support vector machine (SVM) to estimate the state-of-charge (SOC) of a large-scale lithium-ion-polymer (LiP) battery pack. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current and voltage. The coulomb counting SOC estimator has been used in many applications but it has many drawbacks [S. Piller, M. Perrin, Methods for state-of-charge determination and their application, J. Power Sources 96 (2001) 113-120]. The proposed SVM based solution not only removes the drawbacks of the coulomb counting SOC estimator but also produces accurate SOC estimates, using industry standard US06 [V.H. Johnson, A.A. Pesaran, T. Sack, Temperature-dependent battery models for high-power lithium-ion batteries, in: Presented at the 17th Annual Electric Vehicle Symposium Montreal, Canada, October 15-18, 2000. The paper is downloadable at website http://www.nrel.gov/docs/fy01osti/28716.pdf] aggressive driving cycle test procedures. The proposed SOC estimator extracts support vectors from a battery operation history then uses only these support vectors to estimate SOC, resulting in minimal computation load and suitable for real-time embedded system applications.
Characterization of digital medical images utilizing support vector machines
Directory of Open Access Journals (Sweden)
Zafiropoulos Elias P
2004-03-01
Full Text Available Abstract Background In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. Methods The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. Results The SVM (Support Vector Machines algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi, while the neural networks performed approximately the same. Conclusion The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.
Twin Support Vector Machine: A review from 2007 to 2014
Directory of Open Access Journals (Sweden)
Divya Tomar
2015-03-01
Full Text Available Twin Support Vector Machine (TWSVM is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM. TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM.
Approximate entropy and support vector machines for electroencephalogram signal classification*****
Institute of Scientific and Technical Information of China (English)
Zhen Zhang; Yi Zhou; Ziyi Chen; Xianghua Tian; Shouhong Du; Ruimei Huang
2013-01-01
The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epi-leptic seizures were included in this study. They were al diagnosed with neocortex localized epi-lepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was con-structed with the approximate entropy extracted from one epileptic case, and then electroence-phalogram waves of the other three cases were classified, reaching a 93.33%accuracy rate. Our findings suggest that the use of approximate entropy al ows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
Institute of Scientific and Technical Information of China (English)
胡志坤; 桂卫华; 彭小奇
2004-01-01
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.
Support vector machine based fault classification and location of a long transmission line
Directory of Open Access Journals (Sweden)
Papia Ray
2016-09-01
Full Text Available This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21% and least fault distance estimation error (0.29%.
Portfolio Optimization with Target-Shortfall-Probability Vector
Directory of Open Access Journals (Sweden)
Leo Schubert
2002-06-01
Full Text Available Traditional portfolio optimization uses the standard deviation of the returns as a measure of risk. In recent years, the Target-Shortfall-Probability (TSP was discussed as an alternative measure. From the utility-theoretical point of view, the TSP is not perfect. Furthermore it is criticized due to the insufficient description of the risk. The advantages of the TSP are the usage independent of the distribution and the intuitive understanding by the investor. The use of a TSP-vector reduces an utility-theoretical disadvantage of a single TSP and offers an sufficient description of risk. The developed Mean-TSP-vector model is a mixed-integer linear program. The CPU-Time of the program to get a solution demonstrates that the model is suitable for practical applications. A test of the performance shows, that the average return of the model when used in bear markets is equal to the results of the traditional portfolio optimization but - due to skewness - in bullish markets can achieve better returns.
Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine
Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung
Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.
Indian Academy of Sciences (India)
B T Abe; O O Olugbara; T Marwala
2014-06-01
The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.
Chord Recognition Based on Temporal Correlation Support Vector Machine
Directory of Open Access Journals (Sweden)
Zhongyang Rao
2016-05-01
Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.
A new support vector machine based multiuser detection scheme
Institute of Scientific and Technical Information of China (English)
WANG Yong-jian; ZHAO Hong-lin
2008-01-01
In order to suppress the multiple access interference(MAI)in 3G,which limits the capacity of a CDMA communication system,a fast relevance vector machine(FRVM)is employed in the muhinser detection (MUD)scheme.This method aims to overcome the shortcomings of many ordinary support vector machine (SVM)based MUD schemes,such as the long training time and the inaccuracy of the decision data,and enhance the performance of a CDMA communication system.Computer simulation results demonstrate that the proposed FRVM based muhiuser detection has lower bit error rate,costs short training time,needs fewer kernel functions and possesses better near-far resistance.
Mandarin Digits Speech Recognition Using Support Vector Machines
Institute of Scientific and Technical Information of China (English)
XIE Xiang; KUANG Jing-ming
2005-01-01
A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33%, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.
SOGRA - Supporting Optimized GNSS Research in Africa
2014-12-08
AFRL-AFOSR-UK-TR-2015-0022 SOGRA – Supporting Optimized GNSS Research in Africa Rui M. Fernandes Universidade da Beira...DATES COVERED (From – To) 15 August 2009 – 8 December 2014 4. TITLE AND SUBTITLE SOGRA – Supporting Optimized GNSS Research in Africa 5a...University of Beira Interior) supported by EOARD (European Office of Aerospace Research & Development). The major initial investment concerning the
Support Vector Machines for decision support in electricity markets׳ strategic bidding
DEFF Research Database (Denmark)
Pinto, Tiago; Sousa, Tiago M.; Praça, Isabel
2015-01-01
. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...... – Iberian market operator....
Cardiovascular Response Identification Based on Nonlinear Support Vector Regression
Wang, Lu; Su, Steven W.; Chan, Gregory S. H.; Celler, Branko G.; Cheng, Teddy M.; Savkin, Andrey V.
This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set.
MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM.Combined the LS-SVM with the Multi-Resolution Analysis (MRA), this letter proposes the Multi-resolution LS-SVM (MLS-SVM). The proposed algorithm has the same theoretical framework as MRA but with better approximation ability. At a fixed scale MLS-SVM is a classical LS-SVM, but MLS-SVM can gradually approximate the target function at different scales. In experiments, the MLS-SVM is used for nonlinear system identification, and achieves better identification accuracy.
Debris Flow Hazard Assessment Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
YUAN Lifeng; ZHANG Youshui
2006-01-01
Seven factors, including the maximum volume of once flow , occurrence frequency of debris flow , watershed area , main channel length , watershed relative height difference , valley incision density and the length ratio of sediment supplement are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, we selected 259 basic data of 37 debris flow channels in Yunnan Province as learning samples in this study. We create a debris flow hazard assessment model based on SVM. The model was validated though instance applications and showed encouraging results.
Support vector machine for predicting protein interactions using domain scores
Institute of Scientific and Technical Information of China (English)
PENG Xin-jun; WANG Yi-fei
2009-01-01
Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches.
Estimation of underdetermined mixing matrix based on support vector machine
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In underdetermined blind source separation (BSS), a novel algorithm based on extended support vector machine(SVM) is proposed to estimate the mixing matrix in this paper, including the number of the active sources. Instead of traditional clustering algorithms, it mainly takes the modulus of observations and the number in each direction of arrival, without any prior knowledge about the sources except for sparsity, and it is not sensitive to the initial values. Simulations are given to illustrate availability and robustness of our algorithm.
Support vector machine classifiers for large data sets.
Energy Technology Data Exchange (ETDEWEB)
Gertz, E. M.; Griffin, J. D.
2006-01-31
This report concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. Several methods are proposed based on interior point methods for convex quadratic programming. Software implementations are developed by adapting the object-oriented packaging OOQP to the problem structure and by using the software package PETSc to perform time-intensive computations in a distributed setting. Linear systems arising from classification problems with moderately large numbers of features are solved by using two techniques--one a parallel direct solver, the other a Krylov-subspace method incorporating novel preconditioning strategies. Numerical results are provided, and computational experience is discussed.
Cross-Validation, Bootstrap, and Support Vector Machines
Directory of Open Access Journals (Sweden)
Masaaki Tsujitani
2011-01-01
Full Text Available This paper considers the applications of resampling methods to support vector machines (SVMs. We take into account the leaving-one-out cross-validation (CV when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.
Probability output of multi-class support vector machines
Institute of Scientific and Technical Information of China (English)
忻栋; 吴朝晖; 潘云鹤
2002-01-01
A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.
Slope Deformation Prediction Based on Support Vector Machine
Directory of Open Access Journals (Sweden)
Lei JIA
2013-07-01
Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process，explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.
Robust Source Localization in Shallow Water Based on Vector Optimization
Institute of Scientific and Technical Information of China (English)
SONG Hai-yan; SHI Jie; LIU Bo-sheng
2013-01-01
Owing to the multipath effect,the source localization in shallow water has been an area of active interest.However,most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty,which limit their further application in practical engineering.In this paper,a new method of source localization in shallow water,based on vector optimization concept,is described,which is highly robust against environmental factors affecting the localization,such as the channel depth,the bottom reflection coefficients,and so on.Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom,the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation.It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints.It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method,such as the software tool,SeDuMi.Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.
Robust source localization in shallow water based on vector optimization
Song, Hai-yan; Shi, Jie; Liu, Bo-sheng
2013-06-01
Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.
Optimal control strategy of malaria vector using genetically modified mosquitoes.
Rafikov, M; Bevilacqua, L; Wyse, A P P
2009-06-07
The development of transgenic mosquitoes that are resistant to diseases may provide a new and effective weapon of diseases control. Such an approach relies on transgenic mosquitoes being able to survive and compete with wild-type populations. These transgenic mosquitoes carry a specific code that inhibits the plasmodium evolution in its organism. It is said that this characteristic is hereditary and consequently the disease fades away after some time. Once transgenic mosquitoes are released, interactions between the two populations and inter-specific mating between the two types of mosquitoes take place. We present a mathematical model that considers the generation overlapping and variable environment factors. Based on this continuous model, the malaria vector control is formulated and solved as an optimal control problem, indicating how genetically modified mosquitoes should be introduced in the environment. Numerical simulations show the effectiveness of the proposed control.
About the use of vector optimization for company's contractors selection
Medvedeva, M. A.; Medvedev, M. A.
2017-07-01
For effective functioning of an enterprise it is necessary to make a right choice of partners: suppliers of raw material, buyers of finished products, and others with which the company interacts in the course of their business. However, the presence on the market of big amount of enterprises makes the choice of the most appropriate among them very difficult and requires the ability to objectively assess of the possible partners, based on multilateral analysis of their activities. This analysis can be carried out based on the solution of multiobjective problem of mathematical programming by using the methods of vector optimization. The present work addresses the theoretical foundations of such approach and also describes an algorithm realizing proposed method on practical example.
Optimization of conditions for transfection with the Sofast gene vector.
Zhou, Lei; Liu, Fan; Qiao, Fang-Fang; Tong, Man-Li; Fu, Zuo-Gen; Dan, Bing; Yang, Tian-Ci; Zhang, Zhong-Ying
2011-01-01
We previously reported the synthesis and characterization of a novel cationic polymer gene vector. The present article further explored and optimized the working conditions of the Sofast gene vector both in vitro and in vivo, and improved its performance. The transfection conditions of Sofast, such as cell type, cell density, transfection time, N/P values and analysis time after transfection, were further explored. Moreover, the effects of the fusion peptide diINF-7 on transfection efficiency were examined. Sofast was successfully applied for the transfection of exogenous genes into more than 40 types of cell lines derived from humans, mice, monkeys and other species. When the cells were 50-80% confluent, Sofast possessed a better transfection efficiency. In most cases, Sofast also had a higher transfection efficiency when it was used to transfect cells that were seeded for several hours and had adhered to the substrate. The results from in vitro experiments indicate that the recommended Sofast to DNA mass ratio is 16:1, and the optimum analysis time after transfection is 48 h. The salt concentration in the Sofast working solution markedly affected the transfection efficiency. When conducting in vivo transfection, the working solution should be salt-free, whereas for in vitro transfection, it is more appropriate for the working solution to include certain salt concentrations. Finally, the results confirm that diINF-7 significantly promotes the transfection efficiency of Sofast. In conclusion, the present research not only established the optimal conditions for Sofast in the transfection of commonly used cells, but also built the foundations for in vivo and in vitro applications of Sofast, as well as its use in clinical practice.
The new interpretation of support vector machines on statistical learning theory
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper is concerned with the theoretical foundation of support vector machines (SVMs). The purpose is to develop further an exact relationship between SVMs and the statistical learning theory (SLT). As a representative, the standard C-support vector classification (C-SVC) is considered here. More precisely, we show that the decision function obtained by C-SVC is just one of the decision functions obtained by solving the optimization problem derived directly from the structural risk minimization principle. In addition, an interesting meaning of the parameter C in C-SVC is given by showing that C corresponds to the size of the decision function candidate set in the structural risk minimization principle.
Recursive Feature Selection with Significant Variables of Support Vectors
Directory of Open Access Journals (Sweden)
Chen-An Tsai
2012-01-01
Full Text Available The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE and recursive support vector machine (RSVM. The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.
Novel cascade FPGA accelerator for support vector machines classification.
Papadonikolakis, Markos; Bouganis, Christos-Savvas
2012-07-01
Support vector machines (SVMs) are a powerful machine learning tool, providing state-of-the-art accuracy to many classification problems. However, SVM classification is a computationally complex task, suffering from linear dependencies on the number of the support vectors and the problem's dimensionality. This paper presents a fully scalable field programmable gate array (FPGA) architecture for the acceleration of SVM classification, which exploits the device heterogeneity and the dynamic range diversities among the dataset attributes. An adaptive and fully-customized processing unit is proposed, which utilizes the available heterogeneous resources of a modern FPGA device in efficient way with respect to the problem's characteristics. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 2-3 orders of magnitude, compared to the CPU implementation. The proposed architecture outperforms other proposed FPGA and graphic processor unit approaches by more than seven times. Furthermore, based on the special properties of the heterogeneous architecture, this paper introduces the first FPGA-oriented cascade SVM classifier scheme, which exploits the FPGA reconfigurability and intensifies the custom-arithmetic properties of the heterogeneous architecture. The results show that the proposed cascade scheme is able to increase the heterogeneous classifier throughput even further, without introducing any penalty on the resource utilization.
Biologically relevant neural network architectures for support vector machines.
Jändel, Magnus
2014-01-01
Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme. Copyright © 2013 Elsevier Ltd. All rights reserved.
Least squares weighted twin support vector machines with local information
Institute of Scientific and Technical Information of China (English)
花小朋; 徐森; 李先锋
2015-01-01
A least squares version of the recently proposed weighted twin support vector machine with local information (WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information (LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.
Support Vector Machine Classification of Drunk Driving Behaviour
Chen, Huiqin; Chen, Lei
2017-01-01
Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN), the root mean square value of the difference of the adjacent R–R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.
Reducing Support Vector Machine Classification Error by Implementing Kalman Filter
Directory of Open Access Journals (Sweden)
Muhsin Hassan
2013-08-01
Full Text Available The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.
Support Vector Machine Classification of Drunk Driving Behaviour
Directory of Open Access Journals (Sweden)
Huiqin Chen
2017-01-01
Full Text Available Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN, the root mean square value of the difference of the adjacent R–R interval series (RMSSD, low frequency (LF, high frequency (HF, the ratio of the low and high frequencies (LF/HF, and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.
Support Vector Machine Classification of Drunk Driving Behaviour.
Chen, Huiqin; Chen, Lei
2017-01-23
Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R-R intervals (SDNN), the root mean square value of the difference of the adjacent R-R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.
Interpreting linear support vector machine models with heat map molecule coloring
Directory of Open Access Journals (Sweden)
Rosenbaum Lars
2011-03-01
Full Text Available Abstract Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.
Support Vector Machine Diagnosis of Acute Abdominal Pain
Björnsdotter, Malin; Nalin, Kajsa; Hansson, Lars-Erik; Malmgren, Helge
This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians' results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.
Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat
2015-01-01
Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.
Zhou, Lim Yi; Shan, Fam Pei; Shimizu, Kunio; Imoto, Tomoaki; Lateh, Habibah; Peng, Koay Swee
2017-08-01
A comparative study of logistic regression, support vector machine (SVM) and least square support vector machine (LSSVM) models has been done to predict the slope failure (landslide) along East-West Highway (Gerik-Jeli). The effects of two monsoon seasons (southwest and northeast) that occur in Malaysia are considered in this study. Two related factors of occurrence of slope failure are included in this study: rainfall and underground water. For each method, two predictive models are constructed, namely SOUTHWEST and NORTHEAST models. Based on the results obtained from logistic regression models, two factors (rainfall and underground water level) contribute to the occurrence of slope failure. The accuracies of the three statistical models for two monsoon seasons are verified by using Relative Operating Characteristics curves. The validation results showed that all models produced prediction of high accuracy. For the results of SVM and LSSVM, the models using RBF kernel showed better prediction compared to the models using linear kernel. The comparative results showed that, for SOUTHWEST models, three statistical models have relatively similar performance. For NORTHEAST models, logistic regression has the best predictive efficiency whereas the SVM model has the second best predictive efficiency.
Using support vector machines for anomalous change detonation
Energy Technology Data Exchange (ETDEWEB)
Theiler, James P [Los Alamos National Laboratory; Steinwart, Ingo [UNIV STUTTGART; Llamocca, Daniel [UNM
2010-01-01
We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which effectively assume a Gaussian distribution. In this paper, we investigate the use of support vector machines (SVMs) with radial basis kernels for finding anomalous changes. Compared to typical applications of SVMs, we are operating in a regime of very low false alarm rate. This means that even for relatively large training sets, the data are quite meager in the regime of operational interest. This drives us to use larger training sets, which in turn places more of a computational burden on the SVM. We initially considered three different approaches to to address the need to work in the very low false alarm rate regime. The first is a standard SVM which is trained at one threshold (where more reliable estimates of false alarm rates are possible) and then re-thresholded for the low false alarm rate regime. The second uses the same thresholding approach, but employs a so-called least squares SVM; here a quadratic (instead of a hinge-based) loss function is employed, and for this model, there are good theoretical arguments in favor of adjusting the threshold in a straightforward manner. The third approach employs a weighted support vector machine, where the weights for the two types of errors (false alarm and missed detection) are automatically adjusted to achieve the desired false alarm rate. We have found in previous experiments (not shown here) that the first two types can in some cases work well, while in other cases they do not. This renders both approaches unreliable for automated change detection. By contrast, the third approach reliably produces good results, but at
Cavitation detection of butterfly valve using support vector machines
Yang, Bo-Suk; Hwang, Won-Woo; Ko, Myung-Han; Lee, Soo-Jong
2005-10-01
Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur, resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, monitoring of cavitation is of economic interest and is very important in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals acquired from butterfly valves in the pumping stations. And the classification success rate is compared with that of self-organizing feature map neural network (SOFM).
Online Handwritten Sanskrit Character Recognition Using Support Vector Classification
Directory of Open Access Journals (Sweden)
Prof. Sonal P.Patil
2014-05-01
Full Text Available Handwritten recognition has been one of the active and challenging research areas in the field of image processing. In this Paper, we are going to analyses feature extraction technique to recognize online handwritten Sanskrit word using preprocessing, segmentation. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts and languages such as Sanskrit has disadvantaged information extraction from a large body of documents of cultural and historical importance. Here we use Freeman chain code (FCC as the representation technique of an image character. Chain code gives the boundary of a character image in which the codes represents the direction of where is the location of the next pixel. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criterion of features toinput the classification is the chain code that converted to various features. And segmentation is applied to evaluate the possible segmentation zone. Accordingly, several generations are performed to evaluate the individuals with maximum fitness value. Support vector machine (SVM is chosen for the classification step.
Support vector machine approach for protein subcellular localization prediction.
Hua, S; Sun, Z
2001-08-01
Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
Finger vein image quality evaluation using support vector machines
Yang, Lu; Yang, Gongping; Yin, Yilong; Xiao, Rongyang
2013-02-01
In an automatic finger-vein recognition system, finger-vein image quality is significant for segmentation, enhancement, and matching processes. In this paper, we propose a finger-vein image quality evaluation method using support vector machines (SVMs). We extract three features including the gradient, image contrast, and information capacity from the input image. An SVM model is built on the training images with annotated quality labels (i.e., high/low) and then applied to unseen images for quality evaluation. To resolve the class-imbalance problem in the training data, we perform oversampling for the minority class with random-synthetic minority oversampling technique. Cross-validation is also employed to verify the reliability and stability of the learned model. Our experimental results show the effectiveness of our method in evaluating the quality of finger-vein images, and by discarding low-quality images detected by our method, the overall finger-vein recognition performance is considerably improved.
Application of Support Vector Machine to Ship Steering
Institute of Scientific and Technical Information of China (English)
LUO Wei-lin; ZOU Zao-jian; LI Tie-shan
2009-01-01
System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response models of ship steering motion, including the first order linear, the first order nonlinear, the second order linear and the second order nonlinear models. Predictions of manoeuvres including trained samples by using the identified parameters are compared with the results of free-running model tests. It is discussed that the different four categories are consistent with each other both analytically and numerically. The generalization of the identified model is verified by predicting different untrained manoeuvres. The simulations and comparisons demonstrate the validity of the proposed method.
BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES
Directory of Open Access Journals (Sweden)
V. Dheepa
2012-07-01
Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.
Support Vector Machine active learning for 3D model retrieval
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user's semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.
Temperature prediction control based on least squares support vector machines
Institute of Scientific and Technical Information of China (English)
Bin LIU; Hongye SU; Weihua HUANG; Jian CHU
2004-01-01
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity.The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel.In the process of system running,the off-line model is linearized at each sampling instant,and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant.The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay.The results of the experiment verify the effectiveness and merit of the algorithm.
Using support vector classification for SAR of fentanyl derivatives
Institute of Scientific and Technical Information of China (English)
Ning DONG; Wen-cong LU; Nian-yi CHEN; You-cheng ZHU; Kai-xian CHEN
2005-01-01
Aim: To discriminate between fentanyl derivatives with high and low activities.Methods: The support vector classification (SVC) method, a novel approach,was employed to investigate structure-activity relationship (SAR) of fentanyl derivatives based on the molecular descriptors, which were quantum parameters including △E [energy difference between highest occupied molecular orbital energy (HOMO) and lowest empty molecular orbital energy (LUMO)], MR(molecular refractivity) and Mr (molecular weight). Results: By using leave-oneout cross-validation test, the accuracies of prediction for activities of fentanyl derivatives in SVC, principal component analysis (PCA), artificial neural network (ANN) and K-nearest neighbor (KNN) models were 93%, 86%, 57%, and 71%, respectively. The results indicated that the performance of the SVC model was better than those of PCA, ANN, and KNN models for this data. Conclusion:SVC can be used to investigate SAR of fentanyl derivatives and could be a promising tool in the field of SAR research.
Packet Classification using Support Vector Machines with String Kernels
Directory of Open Access Journals (Sweden)
Sarthak Munshi
2016-08-01
Full Text Available Since the inception of internet many methods have been devised to keep untrusted and malicious packets away from a user’s system . The traffic / packet classification can be used as an important tool to detect intrusion in the system. Using Machine Learning as an efficient statistical based approach for classifying packets is a novel method in practice today . This paper emphasizes upon using an advanced string kernel method within a support vector machine to classify packets .There exists a paper related to a similar problem using Machine Learning [2]. But the researches mentioned in their paper are not up-to date and doesn’t account for modern day string kernels that are much more efficient . My work extends their research by introducing different approaches to classify encrypted / unencrypted traffic / packets .
Support vector classifier based on principal component analysis
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Support vector classifier (SVC) has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC.A principal component analysis (PCA) is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently,and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC.Furthermore,a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines.Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically,but also improves the identify rates effectively.
On the regularization path of the support vector domain description
DEFF Research Database (Denmark)
Hansen, Michael Sass; Sjöstrand, Karl; Larsen, Rasmus
2010-01-01
The internet and a growing number of increasingly sophisticated measuring devices make vast amounts of data available in many applications. However, the dimensionality is often high, and the time available for manual labelling scarce. Methods for unsupervised novelty detection are a great step...... towards meeting these challenges, and the support vector domain description has already shown its worth in this field. The method has recently received more attention, since it has been shown that the regularization path is piece-wise linear, and can be calculated efficiently. The presented work restates...... the new findings in a manner which permits the calculation with O(n.n(B)) complexity in each iteration step instead of O(n(2) + n(B)(3)), where n is the number of data points and n, is the number of boundary points. This is achieved by updating and downdating the system matrix to avoid redundant...
Application of support vector machine to synthetic earthquake prediction
Institute of Scientific and Technical Information of China (English)
Chun Jiang; Xueli Wei; Xiaofeng Cui; Dexiang You
2009-01-01
This paper introduces the method of support vector machine (SVM) into the field of synthetic earthquake prediction, which is a non-linear and complex seismogenic system. As an example, we apply this method to predict the largest annual magnitude for the North China area (30°E-42°E, 108°N-125°N) and the capital region (38°E-41.5°E, 114°N-120°N) on the basis of seismicity parameters and observed precursory data. The corresponding prediction rates for the North China area and the capital region are 64.1% and 75%, respectively, which shows that the method is feasible.
SENSITIVITY ANALYSIS FOR ROLLING PROCESS BASED ON SUPPORT VECTOR MACHINE
Institute of Scientific and Technical Information of China (English)
Huang Yanwei; Wu Tihua; Zhao Jingyi; Wang Yiqun
2005-01-01
A method for the calculation of the sensitivity factors of the rolling process has been obtained by differentiating the roll force model based on support vector machine. It can eliminate the algebraic loop of the analytical model of the rolling process. The simulations in the first stand of five stand cold tandem rolling mill indicate that the calculation for sensitivities by this proposed method can obtain a good accuracy, and an appropriate adjustment on the control variables determined directly by the sensitivity has an excellent compensation accuracy. Moreover, the roll gap has larger effect on the exit thickness than both front tension and back tension, and it is more efficient to select the roll gap as the controlvariable of the thickness control system in the first stand.
Application of Support Vector Machine to Forex Monitoring
Kamruzzaman, Joarder; Sarker, Ruhul A.
Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.
The seam offset identification based on support vector regression machines
Institute of Scientific and Technical Information of China (English)
Zeng Songsheng; Shi Yonghua; Wang Guorong; Huang Guoxing
2009-01-01
The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filtering from the longitudinal direction.
Threat Assessment of Targets Based on Support Vector Machine
Institute of Scientific and Technical Information of China (English)
CAI Huai-ping; LIU Jing-xu; CHEN Ying-wu
2006-01-01
In the context of cooperative engagement of armored vehicles, the threat factors of offensive targets are analyzed, and a threat assessment (TA) model is built based on a support v.ector machine (SVM) method. The SVM-based model has some advantages over the traditional method-based models: the complex factors of threat are considered in the cooperative engagement; the shortcomings of neural networks, such as local minimum and "over fitting", are overcome to improve the generalization ability; its operation speed is high and meets the needs of real time C2 of cooperative engagement; the assessment results could be more reasonable because of its self-learning capability. The analysis and simulation indicate that the SVM method is an effective method to resolve the TA problems.
FORECASTING NIKKEI 225 INDEX WITH SUPPORT VECTOR MACHINE
Institute of Scientific and Technical Information of China (English)
HUANG Wei; Yoshiteru Nakamori; WANG Shouyang; YU Lean
2003-01-01
Support Vector Machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare the performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms other classification methods. Furthermore, we propose a combining model by integrating SVM with other classification methods. The combining model performs the best among the forecasting methods.
Sensitivity of Support Vector Machine Classification to Various Training Features
Directory of Open Access Journals (Sweden)
Fuling Bian
2013-07-01
Full Text Available Remote sensing image classification is one of the most important techniques in image interpretation, which can be used for environmental monitoring, evaluation and prediction. Many algorithms have been developed for image classification in the literature. Support vector machine (SVM is a kind of supervised classification that has been widely used recently. The classification accuracy produced by SVM may show variation depending on the choice of training features. In this paper, SVM was used for land cover classification using Quickbird images. Spectral and textural features were extracted for the classification and the results were analyzed thoroughly. Results showed that the number of features employed in SVM was not the more the better. Different features are suitable for different type of land cover extraction. This study verifies the effectiveness and robustness of SVM in the classification of high spatial resolution remote sensing images.
Support vector machine ensemble using rough sets theory
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A support vector machine (SVM) ensemble classifier is proposed. Performance of SVM trained in an input space consisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decisionmaking of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.
TYRE DYNAMICS MODELLING OF VEHICLE BASED ON SUPPORT VECTOR MACHINES
Institute of Scientific and Technical Information of China (English)
ZHENG Shuibo; TANG Houjun; HAN Zhengzhi; ZHANG Yong
2006-01-01
Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines (SVMs). The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation(BP) neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMs-tyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simulation.
Support vector echo-state machine for chaotic time-series prediction.
Shi, Zhiwei; Han, Min
2007-03-01
A novel chaotic time-series prediction method based on support vector machines (SVMs) and echo-state mechanisms is proposed. The basic idea is replacing "kernel trick" with "reservoir trick" in dealing with nonlinearity, that is, performing linear support vector regression (SVR) in the high-dimension "reservoir" state space, and the solution benefits from the advantages from structural risk minimization principle, and we call it support vector echo-state machines (SVESMs). SVESMs belong to a special kind of recurrent neural networks (RNNs) with convex objective function, and their solution is global, optimal, and unique. SVESMs are especially efficient in dealing with real life nonlinear time series, and its generalization ability and robustness are obtained by regularization operator and robust loss function. The method is tested on the benchmark prediction problem of Mackey-Glass time series and applied to some real life time series such as monthly sunspots time series and runoff time series of the Yellow River, and the prediction results are promising.
Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines
Institute of Scientific and Technical Information of China (English)
Jin Weidong; Zhang Gexiang; Hu Laizhao
2006-01-01
This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform (WPT) is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines (SVMs) are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.
Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)
Zhang, Qigui; Deng, Kai
2016-12-01
As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.
Application of support vector machine and quantum genetic algorithm in infrared target recognition
Wang, Hongliang; Huang, Yangwen; Ding, Haifei
2010-08-01
In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.
Chen, S; Samingan, A K; Hanzo, L
2001-01-01
The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.
2016-01-01
Documento que contiene la explicación sobre las temáticas de Sistemas coordenados, Cantidades vectoriales y escalares, Algunas propiedades de los vectores, Componentes de un vector y vectores unitarios
Ecological Footprint Model Using the Support Vector Machine Technique
Ma, Haibo; Chang, Wenjuan; Cui, Guangbai
2012-01-01
The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance. PMID:22291949
Directory of Open Access Journals (Sweden)
Torsten Mattfeldt
2004-01-01
Full Text Available The subclassification of incidental prostatic carcinoma into the categories T1a and T1b is of major prognostic and therapeutic relevance. In this paper an attempt was made to find out which properties mainly predispose to these two tumor categories, and whether it is possible to predict the category from a battery of clinical and histopathological variables using newer methods of multivariate data analysis. The incidental prostatic carcinomas of the decade 1990–99 diagnosed at our department were reexamined. Besides acquisition of routine clinical and pathological data, the tumours were scored by immunohistochemistry for proliferative activity and p53‐overexpression. Tumour vascularization (angiogenesis and epithelial texture were investigated by quantitative stereology. Learning vector quantization (LVQ and support vector machines (SVM were used for the purpose of prediction of tumour category from a set of 10 input variables (age, Gleason score, preoperative PSA value, immunohistochemical scores for proliferation and p53‐overexpression, 3 stereological parameters of angiogenesis, 2 stereological parameters of epithelial texture. In a stepwise logistic regression analysis with the tumour categories T1a and T1b as dependent variables, only the Gleason score and the volume fraction of epithelial cells proved to be significant as independent predictor variables of the tumour category. Using LVQ and SVM with the information from all 10 input variables, more than 80 of the cases could be correctly predicted as T1a or T1b category with specificity, sensitivity, negative and positive predictive value from 74–92%. Using only the two significant input variables Gleason score and epithelial volume fraction, the accuracy of prediction was not worse. Thus, descriptive and quantitative texture parameters of tumour cells are of major importance for the extent of propagation in the prostate gland in incidental prostatic adenocarcinomas. Classical
DEFF Research Database (Denmark)
Le, T.H.A.; Pham, D. T.; Canh, Nam Nguyen;
2010-01-01
Both the efficient and weakly efficient sets of an affine fractional vector optimization problem, in general, are neither convex nor given explicitly. Optimization problems over one of these sets are thus nonconvex. We propose two methods for optimizing a real-valued function over the efficient a...
Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression
Institute of Scientific and Technical Information of China (English)
YE Mei-Ying; WANG Xiao-Dong
2005-01-01
A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.
Directory of Open Access Journals (Sweden)
Hong-Hai Tran
2014-01-01
Full Text Available Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM algorithm is utilized to classify grouting activities into two classes: success and failure. Meanwhile, the differential evolution (DE optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.
Engels, Elien B; Strik, Marc; van Middendorp, Lars B; Kuiper, Marion; Vernooy, Kevin; Prinzen, Frits W
2017-08-01
Proper optimization of atrioventricular (AV) and interventricular (VV) intervals can improve the response to cardiac resynchronization therapy (CRT). It has been demonstrated that the area of the QRS complex (QRSarea) extracted from the vectorcardiogram can be used as a predictor of optimal CRT-device settings. We explored the possibility of extracting vectors from the electrograms (EGMs) obtained from pacing electrodes and of using these EGM-based vectors (EGMVs) to individually optimize acute hemodynamic CRT response. Biventricular pacing was performed in 13 dogs with left bundle branch block (LBBB) of which five also had myocardial infarction (MI), using 100 randomized AV- and VV-settings. Settings providing an acute increase in LV dP/dtmax ≥ 90% of the highest achieved value were defined as optimal. The prediction capability of QRSarea derived from the EGMV (EGMV-QRSarea) was compared with that of QRS duration. EGMV-QRSarea strongly correlated to the change in LV dP/dtmax (R = -0.73 ± 0.19 [LBBB] and -0.66 ± 0.14 [LBBB + MI]), while QRS duration was more poorly related to LV dP/dtmax changes (R = -0.33 ± 0.25 [LBBB] and -0.47 ± 0.39 [LBBB + MI]). This resulted in a better prediction of optimal CRT-device settings by EGMV-QRSarea than by QRS duration (LBBB: AUC = 0.89 [0.86-0.93] vs. 0.76 [0.69-0.83], P < 0.01; LBBB + MI: AUC = 0.91 [0.84-0.99] vs. 0.82 [0.59-1.00], P = 0.20, respectively). In canine hearts with chronic LBBB with or without MI, the EGMV-QRSarea predicts acute hemodynamic CRT response and identifies optimal AV and VV settings accurately. These data support the potency of EGM-based vectors as a noninvasive, easy and patient-tailored tool to optimize CRT-device settings. © 2017 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Zhan-bo Chen
2014-01-01
Full Text Available In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.
[Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].
Kadyrova, N O; Pavlova, L V
2015-01-01
Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.
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Zhongliang Lv
2016-01-01
Full Text Available A novel fault diagnosis method based on variational mode decomposition (VMD and multikernel support vector machine (MKSVM optimized by Immune Genetic Algorithm (IGA is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.
Spatio-temporal avalanche forecasting with Support Vector Machines
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A. Pozdnoukhov
2011-02-01
Full Text Available This paper explores the use of the Support Vector Machine (SVM as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.
Phone Duration Modeling of Affective Speech Using Support Vector Regression
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Alexandros Lazaridis
2012-07-01
Full Text Available In speech synthesis accurate modeling of prosody is important for producing high quality synthetic speech. One of the main aspects of prosody is phone duration. Robust phone duration modeling is a prerequisite for synthesizing emotional speech with natural sounding. In this work ten phone duration models are evaluated. These models belong to well known and widely used categories of algorithms, such as the decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms. Furthermore, we investigate the effectiveness of Support Vector Regression (SVR in phone duration modeling in the context of emotional speech. The evaluation of the eleven models is performed on a Modern Greek emotional speech database which consists of four categories of emotional speech (anger, fear, joy, sadness plus neutral speech. The experimental results demonstrated that the SVR-based modeling outperforms the other ten models across all the four emotion categories. Specifically, the SVR model achieved an average relative reduction of 8% in terms of root mean square error (RMSE throughout all emotional categories.
Hybrid Support Vector Machines-Based Multi-fault Classification
Institute of Scientific and Technical Information of China (English)
GAO Guo-hua; ZHANG Yong-zhong; ZHU Yu; DUAN Guang-huang
2007-01-01
Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 (one versus one) and 1-v-r (one versus rest), are respectively adopted at different classification levels. At the parallel classification level, using 1-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
Nonlinear structural damage detection using support vector machines
Xiao, Li; Qu, Wenzhong
2012-04-01
An actual structure including connections and interfaces may exist nonlinear. Because of many complicated problems about nonlinear structural health monitoring (SHM), relatively little progress have been made in this aspect. Statistical pattern recognition techniques have been demonstrated to be competitive with other methods when applied to real engineering datasets. When a structure existing 'breathing' cracks that open and close under operational loading may cause a linear structural system to respond to its operational and environmental loads in a nonlinear manner nonlinear. In this paper, a vibration-based structural health monitoring when the structure exists cracks is investigated with autoregressive support vector machine (AR-SVM). Vibration experiments are carried out with a model frame. Time-series data in different cases such as: initial linear structure; linear structure with mass changed; nonlinear structure; nonlinear structure with mass changed are acquired.AR model of acceleration time-series is established, and different kernel function types and corresponding parameters are chosen and compared, which can more accurate, more effectively locate the damage. Different cases damaged states and different damage positions have been recognized successfully. AR-SVM method for the insufficient training samples is proved to be practical and efficient on structure nonlinear damage detection.
River flow time series using least squares support vector machines
Samsudin, R.; Saad, P.; Shabri, A.
2011-06-01
This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.
Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
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R. Johny Elton
2016-08-01
Full Text Available This paper proposes support vector machine (SVM based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD uses fuzzy entropy (FuzzyEn as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR ranging from −10 dB to 10 dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels.
Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine
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R. Gholami
2012-01-01
Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.
Support Vector Machine Ensemble Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming
2006-01-01
Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.
A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior
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Bin Lu
2015-01-01
Full Text Available This paper presents a Support Vector Regression (SVR approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.
A Semisupervised Support Vector Machines Algorithm for BCI Systems
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Jianzhao Qin
2007-07-01
Full Text Available As an emerging technology, brain-computer interfaces (BCIs bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM algorithm for brain-computer interface (BCI systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.
Fuzzy support vector machines based on linear clustering
Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao
2005-10-01
A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.
Incremental support vector machines for fast reliable image recognition
Energy Technology Data Exchange (ETDEWEB)
Makili, L., E-mail: makili_le@yahoo.com [Instituto Superior Politécnico da Universidade Katyavala Bwila, Benguela (Angola); Vega, J. [Asociación EURATOM/CIEMAT para Fusión, Madrid (Spain); Dormido-Canto, S. [Dpto. Informática y Automática – UNED, Madrid (Spain)
2013-10-15
Highlights: ► A conformal predictor using SVM as the underlying algorithm was implemented. ► It was applied to image recognition in the TJ–II's Thomson Scattering Diagnostic. ► To improve time efficiency an approach to incremental SVM training has been used. ► Accuracy is similar to the one reached when standard SVM is used. ► Computational time saving is significant for large training sets. -- Abstract: This paper addresses the reliable classification of images in a 5-class problem. To this end, an automatic recognition system, based on conformal predictors and using Support Vector Machines (SVM) as the underlying algorithm has been developed and applied to the recognition of images in the Thomson Scattering Diagnostic of the TJ–II fusion device. Using such conformal predictor based classifier is a computationally intensive task since it implies to train several SVM models to classify a single example and to perform this training from scratch takes a significant amount of time. In order to improve the classification time efficiency, an approach to the incremental training of SVM has been used as the underlying algorithm. Experimental results show that the overall performance of the new classifier is high, comparable to the one corresponding to the use of standard SVM as the underlying algorithm and there is a significant improvement in time efficiency.
Classification of Cotton Leaf Spot Disease Using Support Vector Machine
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Prof. Sonal P. Patil
2014-05-01
Full Text Available In order to obtain more value added products, a product quality control is essentially required Many studies show that quality of agriculture products may be reduced from many causes. One of the most important factors of such quality plant diseases. Consequently, minimizing plant diseases allows substantially improving quality of the product Suitable diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this paper I express Technological Strategies uses mobile captured symptoms of Cotton Leaf Spot images and categorize the diseases using support vector machine. The classifier is being trained to achieve intelligent farming, including early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on Segmentation techniques in which, the captured images are processed for enrichment first. Then texture and color Feature extraction techniques are used to extract features such as boundary, shape, color and texture for the disease spots to recognize diseases.
Data filtering with support vector machines in geometric camera calibration.
Ergun, B; Kavzoglu, T; Colkesen, I; Sahin, C
2010-02-01
The use of non-metric digital cameras in close-range photogrammetric applications and machine vision has become a popular research agenda. Being an essential component of photogrammetric evaluation, camera calibration is a crucial stage for non-metric cameras. Therefore, accurate camera calibration and orientation procedures have become prerequisites for the extraction of precise and reliable 3D metric information from images. The lack of accurate inner orientation parameters can lead to unreliable results in the photogrammetric process. A camera can be well defined with its principal distance, principal point offset and lens distortion parameters. Different camera models have been formulated and used in close-range photogrammetry, but generally sensor orientation and calibration is performed with a perspective geometrical model by means of the bundle adjustment. In this study, support vector machines (SVMs) using radial basis function kernel is employed to model the distortions measured for Olympus Aspherical Zoom lens Olympus E10 camera system that are later used in the geometric calibration process. It is intended to introduce an alternative approach for the on-the-job photogrammetric calibration stage. Experimental results for DSLR camera with three focal length settings (9, 18 and 36 mm) were estimated using bundle adjustment with additional parameters, and analyses were conducted based on object point discrepancies and standard errors. Results show the robustness of the SVMs approach on the correction of image coordinates by modelling total distortions on-the-job calibration process using limited number of images.
Priori Information Based Support Vector Regression and Its Applications
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Litao Ma
2015-01-01
Full Text Available In order to extract the priori information (PI provided by real monitored values of peak particle velocity (PPV and increase the prediction accuracy of PPV, PI based support vector regression (SVR is established. Firstly, to extract the PI provided by monitored data from the aspect of mathematics, the probability density of PPV is estimated with ε-SVR. Secondly, in order to make full use of the PI about fluctuation of PPV between the maximal value and the minimal value in a certain period of time, probability density estimated with ε-SVR is incorporated into training data, and then the dimensionality of training data is increased. Thirdly, using the training data with a higher dimension, a method of predicting PPV called PI-ε-SVR is proposed. Finally, with the collected values of PPV induced by underwater blasting at Dajin Island in Taishan nuclear power station in China, contrastive experiments are made to show the effectiveness of the proposed method.
Optimally matching support and perceived spousal sensitivity.
Cutrona, Carolyn E; Shaffer, Philip A; Wesner, Kristin A; Gardner, Kelli A
2007-12-01
Partner sensitivity is an important antecedent of both intimacy (H. T. Reis & P. Shaver, 1988) and attachment (M. D. S. Ainsworth, 1989). On the basis of the optimal matching model of social support (C. E. Cutrona & D. Russell, 1990), support behaviors that "matched" the support goals of the stressed individual were predicted to lead to the perception of partner sensitivity. Predictions were tested with 59 married couples, who engaged in a videotaped self-disclosure task. Matching support was defined as the disclosure of emotions followed by emotional support or a request for information followed by informational support. Partial evidence was found for the predictions. Matching support following the disclosure of emotions was predictive of perceived partner sensitivity. Mismatched support following the disclosure of emotions predicted lower marital satisfaction, through the mediation of partner sensitivity. Matching support following a request for information was not predictive of perceived partner sensitivity, but negative partner responses (e.g., criticism or sarcasm) following a request for information negatively predicted perceptions of partner sensitivity. The importance of considering the context of support transactions is discussed.
PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.
Liu, Xiaoyong; Fu, Hui
2014-01-01
Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.
PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses
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Xiaoyong Liu
2014-01-01
Full Text Available Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM, particle swarm optimization (PSO, and cuckoo search (CS. The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.
Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine
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Jianwei Cui
2014-01-01
Full Text Available This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB with support vector machine (SVM. The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT- based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.
Dougherty, Andrew W.
Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor
Explaining Support Vector Machines: A Color Based Nomogram
Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo
2016-01-01
Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811
An Optimization Model of Tunnel Support Parameters
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Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction
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Fang Su
2013-01-01
Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.
Optimal Thrust Vectoring for an Annular Aerospike Nozzle Project
National Aeronautics and Space Administration — Recent success of an annular aerospike flight test by NASA Dryden has prompted keen interest in providing thrust vector capability to the annular aerospike nozzle...
An optimized procedure greatly improves EST vector contamination removal
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Wu Huan-Bin
2007-11-01
Full Text Available Abstract Background The enormous amount of sequence data available in the public domain database has been a gold mine for researchers exploring various themes in life sciences, and hence the quality of such data is of serious concern to researchers. Removal of vector contamination is one of the most significant operations to obtain accurate sequence data containing only a cDNA insert from the basecalls output by an automatic DNA sequencer. Popular bioinformatics programs to accomplish vector trimming include LUCY, cross_match and SeqClean. Results In a recent study, where the program SeqClean was used to remove vector contamination from our test set of EST data compiled through various library construction systems, however, a significant number of errors remained after preliminary trimming. These errors were later almost completely corrected by simply using a re-linearized form of the cloning vector to compare against the target ESTs. The modified trimming procedure for SeqClean was also compared with the trimming efficiency of the other two popular programs, LUCY2, and cross_match. Using SeqClean with a re-linearized form of the cloning vector significantly surpassed the other two programs in all tested conditions, while the performance of the other two programs was not influenced by the modified procedure. Vector contamination in dbEST was also investigated in this study: 2203 out of the 48212 ESTs sampled from dbEST (2007-04-18 freeze were found to match sequences in UNIVEC. Conclusion Vector contamination remains a serious concern to the data quality in the public sequence database nowadays. Based on the results presented here, we feel that our modified procedure with SeqClean should be recommended to all researchers for the task of vector removal from EST or genomic sequences.
Single face image reconstruction for super resolution using support vector regression
Lin, Haijie; Yuan, Qiping; Chen, Zhihong; Yang, Xiaoping
2016-10-01
In recent years, we have witnessed the prosperity of the face image super-resolution (SR) reconstruction, especially the learning-based technology. In this paper, a novel super-resolution face reconstruction framework based on support vector regression (SVR) about a single image is presented. Given some input data, SVR can precisely predict output class labels. We regard the SR problem as the estimation of pixel labels in its high resolution version. It's effective to put local binary pattern (LBP) codes and partial pixels into input vectors during training models in our work, and models are learnt from a set of high and low resolution face image. By optimizing vector pairs which are used for learning model, the final reconstructed results were advanced. Especially to deserve to be mentioned, we can get more high frequency information by exploiting the cyclical scan actions in the process of both training and prediction. A large number of experimental data and visual observation have shown that our method outperforms bicubic interpolation and some stateof- the-art super-resolution algorithms.
Support vector machine for day ahead electricity price forecasting
Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti
2015-05-01
Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.
CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE
Directory of Open Access Journals (Sweden)
K.-Y. Lee
2016-06-01
Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate
Prediction of cell penetrating peptides by support vector machines.
Directory of Open Access Journals (Sweden)
William S Sanders
2011-07-01
Full Text Available Cell penetrating peptides (CPPs are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs. We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.
Cloud Detection of Optical Satellite Images Using Support Vector Machine
Lee, Kuan-Yi; Lin, Chao-Hung
2016-06-01
Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection
Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.
2010-01-01
Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a
Institute of Scientific and Technical Information of China (English)
牛培峰; 麻红波; 李国强; 马云飞; 陈贵林; 张先臣
2013-01-01
为了控制循环流化床(CFB)锅炉的NOx排放量,以某热电厂300 MW CFB锅炉测试数据为样本,应用支持向量机(SVM)建立NOx排放特性预测模型.针对SVM回归预测需要人为确定相关参数的不足,应用果蝇优化算法(FOA)优化SVM参数,采用不同工况下的样本数据检验FOA-SVM模型的预测性能,并将该模型的预测结果与粒子群算法(PSO)、遗传算法(GA)和万有引力搜索算法(GSA)优化的SVM模型预测结果进行了比较.结果表明:FOA-SVM模型的泛化能力较强,预测精度较高,训练时间较短,可以相对快速、准确地预测NOx排放质量浓度.%To control the NOx emission from circulating fluidized bed (CFB) boilers, a model was established based on test data of a 300 MW thermal power plant using support vector machine (SVM). To overcome the deficiency of SVM regression prediction in artificial determination of relevant parameters, the fruit fly optimization algorithm (FOA) was applied to optimize the SVM parameters. Prediction performance of the FOA-SVM model was then verified with sample data under different experimental conditions, of which the prediction results were compared with those optimized by particle swarm optimization (PSO), genetic algorithm (GA) and gravitation search algorithm (GSA). Results show that the FOA-SVM model has stronger genralization capability, higher prediction accuracy and shorter training time, which may therefore predict the mass concentration of NOx emission quickly and accurately.
Classification of Regional Ionospheric Disturbances Based on Support Vector Machines
Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil
2016-07-01
Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification
DETERMINATION METHOD OF OPTIMAL SUPPORTING TIME IN HEADING FACE
Institute of Scientific and Technical Information of China (English)
杜长龙; 曹红波; 王燕宁; 张艳
1997-01-01
This paper has put forward a concept of optimal supporting time through analysing the influence of the supporting time in the heading face on the supporting result of surrounding rock. The method of the optimal supporting time determined by graphical method is discussed, and the calculating formula for determining the optimal supporting time through the analysis method is derived.
Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei
2015-02-01
We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.
Directory of Open Access Journals (Sweden)
F. Gunes
2016-09-01
Full Text Available In this work, an accurate and reliable S- and Noise (N - parameter black-box models for a microwave transistor are constructed based on the sparse regression using the Support Vector Regression Machine (SVRM as a nonlinear extrapolator trained by the data measured at the typical bias currents belonging to only a single bias voltage in the middle region of the device operation domain of (VDS/VCE, IDS/IC, f. SVRMs are novel learning machines combining the convex optimization theory with the generalization and therefore they guarantee the global minimum and the sparse solution which can be expressed as a continuous function of the input variables using a subset of the training data so called Support Vector (SVs. Thus magnitude and phase of each S- or N- parameter are expressed analytically valid in the wide range of device operation domain in terms of the Characteristic SVs obtained from the substantially reduced measured data. The proposed method is implemented successfully to modelling of the two LNA transistors ATF-551M4 and VMMK 1225 with their large operation domains and the comparative error-metric analysis is given in details with the counterpart method Generalized Regression Neural Network GRNN. It can be concluded that the Characteristic Support Vector based-sparse regression is an accurate and reliable method for the black-box signal and noise modelling of microwave transistors that extrapolates a reduced amount of training data consisting of the S- and N- data measured at the typical bias currents belonging to only a middle bias voltage in the form of continuous functions into the wide operation range.
2016-12-01
ARL-CR-0810 ● DEC 2016 US Army Research Laboratory Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector...not return it to the originator. ARL-CR-0810 ● DEC 2016 US Army Research Laboratory Aerodynamic Optimization of a ...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street
Optimized production and concentration of lentiviral vectors containing large inserts.
al Yacoub, Nadya; Romanowska, Malgorzata; Haritonova, Natalie; Foerster, John
2007-07-01
Generation of high titer lentiviral stocks and efficient virus concentration are central to maximize the utility of lentiviral technology. Here we evaluate published protocols for lentivirus production on a range of transfer vectors differing in size (7.5-13.2 kb). We present a modified virus production protocol robustly yielding useful titers (up to 10(7)/ml) for a range of different transfer vectors containing packaging inserts up to 7.5 kb. Moreover, we find that virus recovery after concentration by ultracentrifugation depends on the size of the packaged inserts, heavily decreasing for large packaged inserts. We describe a fast (4 h) centrifugation protocol at reduced speed allowing high virus recovery even for large and fragile lentivirus vectors. The protocols outlined in the current report should be useful for many labs interested in producing and concentrating high titer lentiviral stocks.
Comparison of ν-support vector regression and logistic equation for ...
African Journals Online (AJOL)
Jane
2011-07-04
Jul 4, 2011 ... DOI: 10.5897/AJB10.2086. ISSN 1684–5315 ... As a novel type of learning method, support ... formalism known as the support vector machines (SVMs) ..... fermentation process using neural networks and genetic algorithms.
DEFF Research Database (Denmark)
Boeriis, Morten; van Leeuwen, Theo
2017-01-01
This article revisits the concept of vectors, which, in Kress and van Leeuwen’s Reading Images (2006), plays a crucial role in distinguishing between ‘narrative’, action-oriented processes and ‘conceptual’, state-oriented processes. The use of this concept in image analysis has usually focused...... on the most salient vectors, and this works well, but many images contain a plethora of vectors, which makes their structure quite different from the linguistic transitivity structures with which Kress and van Leeuwen have compared ‘narrative’ images. It can also be asked whether facial expression vectors...... should be taken into account in discussing ‘reactions’, which Kress and van Leeuwen link only to eyeline vectors. Finally, the question can be raised as to whether actions are always realized by vectors. Drawing on a re-reading of Rudolf Arnheim’s account of vectors, these issues are outlined...
Estimation of the laser cutting operating cost by support vector regression methodology
Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam
2016-09-01
Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.
Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F
2014-06-01
To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).
A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression
Institute of Scientific and Technical Information of China (English)
2012-01-01
To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion （SVR） theory, a novel flatness pattern recognition method based on least squares support vector regression （LS-SVR） was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out （LOO） error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.
Human action recognition with group lasso regularized-support vector machine
Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei
2016-05-01
The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.
Support vector machines for TEC seismo-ionospheric anomalies detection
Directory of Open Access Journals (Sweden)
M. Akhoondzadeh
2013-02-01
Full Text Available Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011, Haiti (12 January 2010 and Samoa (29 September 2009. The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012 obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method
Instability of anisotropic cosmological solutions supported by vector fields.
Himmetoglu, Burak; Contaldi, Carlo R; Peloso, Marco
2009-03-20
Models with vector fields acquiring a nonvanishing vacuum expectation value along one spatial direction have been proposed to sustain a prolonged stage of anisotropic accelerated expansion. Such models have been used for realizations of early time inflation, with a possible relation to the large scale cosmic microwave background anomalies, or of the late time dark energy. We show that, quite generally, the concrete realizations proposed so far are plagued by instabilities (either ghosts or unstable growth of the linearized perturbations) which can be ultimately related to the longitudinal vector polarization present in them. Phenomenological results based on these models are therefore unreliable.
Institute of Scientific and Technical Information of China (English)
刘英培; 栗然; 梁海平
2014-01-01
Traditional PI regulator has some defects. Direct torque control (DTC) for permanent magnet synchronous motor (PMSM) based on active-disturbance rejection control (ADRC) optimized by least squares support vector machines (LSSVM) method was proposed in this paper. The speed regulator based on ADRC was designed with the inputs of given speed and real speed and the output of given electromagnet torque. The Gaussian radial basis kernel function was chosen in the model. The realization of the LSSVM regression model embedded in ADRC regulator was analyzed in-depth and detailed, which optimized the ADRC regulator. The ADRC observation precision and dynamic response are improved. The effect of motor parameters and load disturbances on the system is significantly reduced. The anti-interference ability of the system is further improved. Simulation and experiment results have verified the feasibility and effectiveness of this method.%针对传统PI调节器的缺陷，提出一种基于最小二乘支持向量机(least squares support vector machine，LSSVM)优化自抗扰控制器(active-disturbance rejection control， ADRC)的永磁同步电机直接转矩控制方法。以给定转速和实际转速作为输入信号，以给定电磁转矩作为输出信号，设计了 ADRC 速度调节器；在此基础上，在回归模型中选取高斯径向基核函数，深入分析了将LSSVM回归模型有效嵌入ADRC调节器的实现方法，实现对ADRC控制器的优化，以提高 ADRC 观测精度及系统动态响应速度，很大程度上降低了电机参数变化和负载扰动对系统的影响，进一步改善了系统的抗干扰能力。仿真和实验结果验证了该方法的可行性和有效性。
Chanda, Emmanuel; Mukonka, Victor Munyongwe; Mthembu, David; Kamuliwo, Mulakwa; Coetzer, Sarel; Shinondo, Cecilia Jill
2012-01-01
Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission determinants in the assessment of impact of interventions is critical. This requires availability of relevant spatial and attribute data to support malaria surveillance, monitoring, and evaluation. Monitoring the impact of vector control through a GIS-based decision support (DSS) has revealed spatial relative change in prevalence of infection and vector susceptibility to insecticides and has enabled measurement of spatial heterogeneity of trend or impact. The revealed trends and interrelationships have allowed the identification of areas with reduced parasitaemia and increased insecticide resistance thus demonstrating the impact of resistance on vector control. The GIS-based DSS provides opportunity for rational policy formulation and cost-effective utilization of limited resources for enhanced malaria vector control.
Support vector machine-based feature extractor for L/H transitions in JETa)
González, S.; Vega, J.; Murari, A.; Pereira, A.; Ramírez, J. M.; Dormido-Canto, S.; Jet-Efda Contributors
2010-10-01
Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.
Nonlinear decoupling controller design based on least squares support vector regression
Institute of Scientific and Technical Information of China (English)
WEN Xiang-jun; ZHANG Yu-nong; YAN Wei-wu; XU Xiao-ming
2006-01-01
Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control ora general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is unknown or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.
Xu, Lin; Feng, Yanqiu; Liu, Xiaoyun; Kang, Lili; Chen, Wufan
2014-01-01
Accuracy of interpolation coefficients fitting to the auto-calibrating signal data is crucial for k-space-based parallel reconstruction. Both conventional generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction that utilizes linear interpolation function and nonlinear GRAPPA (NLGRAPPA) reconstruction with polynomial kernel function are sensitive to interpolation window and often cannot consistently produce good results for overall acceleration factors. In this study, sparse multi-kernel learning is conducted within the framework of least squares support vector regression to fit interpolation coefficients as well as to reconstruct images robustly under different subsampling patterns and coil datasets. The kernel combination weights and interpolation coefficients are adaptively determined by efficient semi-infinite linear programming techniques. Experimental results on phantom and in vivo data indicate that the proposed method can automatically achieve an optimized compromise between noise suppression and residual artifacts for various sampling schemes. Compared with NLGRAPPA, our method is significantly less sensitive to the interpolation window and kernel parameters.
A reliability assessment method based on support vector machines for CNC equipment
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.
A SUPPORT VECTOR MACHINE APPROACH FOR DEVELOPING TELEMEDICINE SOLUTIONS: MEDICAL DIAGNOSIS
Directory of Open Access Journals (Sweden)
Mihaela GHEORGHE
2015-06-01
Full Text Available Support vector machine represents an important tool for artificial neural networks techniques including classification and prediction. It offers a solution for a wide range of different issues in which cases the traditional optimization algorithms and methods cannot be applied directly due to different constraints, including memory restrictions, hidden relationships between variables, very high volume of computations that needs to be handled. One of these issues relates to medical diagnosis, a subset of the medical field. In this paper, the SVM learning algorithm is tested on a diabetes dataset and the results obtained for training with different kernel functions are presented and analyzed in order to determine a good approach from a telemedicine perspective.
Comparison on neural networks and support vector machines in suppliers' selection
Institute of Scientific and Technical Information of China (English)
Hu Guosheng; Zhang Guohong
2008-01-01
Suppliers' selection in supply chain management (SCM) has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization,and uncontrolled convergence. A relatively new machine learning technique, support vector machine (SVM), which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers' samples are very insufficient. SVMs are adaptive to deal with small samples' training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.
Improving linearity of position-sensitive detector using support vector machines
Institute of Scientific and Technical Information of China (English)
Meiying Ye
2005-01-01
An intelligent method for improving position linearity of position-sensitive detector (PSD), based on support vector machines (SVMs), is developed. The SVM is established based on the structural risk minimization principle rather than minimizing the empirical error commonly implemented in neural networks.SVM can achieve higher generalization performance. Training SVM is equivalent to solving a linearly constrained quadratic programming problem, thus the solution of SVM is always unique and globally optimal.The improving position linearity procedure has been illustrated using a two-dimensional (2D) PSD. It is pointed out that the position linearity of the measuring system with a proper SVM correction is improved by two orders of magnitude in the measurement range.
Water Quantity Prediction Using Least Squares Support Vector Machines (LS-SVM Method
Directory of Open Access Journals (Sweden)
Nian Zhang
2014-08-01
Full Text Available The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF kernel framework was built on the data set to optimize the tuning parameters and to obtain the moderated output. The training process of LS-SVM was designed to select both kernel parameters and regularization constants. The USGS real-time water data were used as time series input. 50% of the data were used for training, and 50% were used for testing. The experimental results showed that the LS-SVM algorithm is a reliable and efficient method for streamflow prediction, which has an important impact to the water resource management field.
Manivannan, K; Aggarwal, P; Devabhaktuni, V; Kumar, A; Nims, D; Bhattacharya, P
2012-07-15
An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.
Blind multiuser detector for chaos-based CDMA using support vector machine.
Kao, Johnny Wei-Hsun; Berber, Stevan Mirko; Kecman, Vojislav
2010-08-01
The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.
A reliability assessment method based on support vector machines for CNC equipment
Institute of Scientific and Technical Information of China (English)
WU Jun; DENG Chao; SHAO XinYu; XIE S Q
2009-01-01
With the applications of high technology, a catastrophic failure of CNC equipment rarely occurs at normal operation conditions. So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level. This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available. The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment. A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built. A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.
Bifurcations of optimal vector fields in the shallow lake model
T. Kiseleva; F.O.O. Wagener
2010-01-01
The solution structure of the set of optimal solutions of the shallow lake problem, a problem of optimal pollution management, is studied as we vary the values of the system parameters: the natural resilience, the relative importance of the resource for social welfare and the future discount rate. W
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
Xu, S.; Xue, W.; Lin, H.X.
2011-01-01
In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication (SpMV) on NVIDIA GPUs using CUDA. SpMV has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of SpMV based on ELLPACK from
Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
Xu, S.; Xue, W.; Lin, H.X.
2011-01-01
In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication (SpMV) on NVIDIA GPUs using CUDA. SpMV has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of SpMV based on ELLPACK from
Directory of Open Access Journals (Sweden)
Moh. Aries Syufagi
2011-12-01
Full Text Available Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG. CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments using 33 respondent players demonstrates that 61% of players have high trial and error cognitive skill, 21% have high carefully cognitive skill, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the player is emotionally stable. Interests in the players strongly support the procedural learning in a serious game.
On Quadratic Scalarization of One Class of Vector Optimization Problems in Banach Spaces
Directory of Open Access Journals (Sweden)
V. M. Bogomaz
2012-01-01
Full Text Available We study vector optimization problems in partially ordered Banach Spaces. We suppose that the objective mapping possesses a weakened property of lower semicontinuity and make no assumptions on the interior of the ordering cone. We discuss the ”classical” scalarization of vector optimization problems in the form of weighted sum and also we propose other type of scalarization for vector optimization problem, the socalled adaptive scalarization, which inherits some ideas of Pascoletti-Serafini approach. As a result, we show that the scalar nonlinear optimization problems can byturn approximated by the quadratic minimization problems. The advantage of such regularization is especially interesting from a numerical point of view because it gives a possibility to apply rather simple computational methods for the approximation of the whole set of efficient solutions.
[Comparative efficiency of algorithms based on support vector machines for binary classification].
Kadyrova, N O; Pavlova, L V
2015-01-01
Methods of construction of support vector machines require no further a priori infoimation and provide big data processing, what is especially important for various problems in computational biology. The question of the quality of learning algorithms is considered. The main algorithms of support vector machines for binary classification are reviewed and they were comparatively explored for their efficiencies. The critical analysis of the results of this study revealed the most effective support-vector-classifiers. The description of the recommended algorithms, sufficient for their practical implementation, is presented.
Li, Xiaoli; He, Yong; Qiu, Zhengjun; Wu, Di
2008-03-01
This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.
Ensafi, Ali A.; Hasanpour, F.; Khayamian, T.; Mokhtari, A.; Taei, M.
2010-02-01
In this work, a batch chemiluminescence (CL) method has been proposed for the simultaneous determination of two structurally similar alkaloids, noscapine and thebaine. The method is based on the kinetic distinction of the CL reactions of noscapine and thebaine with Ru(bipy) 32+ and Ce(IV) system in a sulfuric acid medium. The least squared support vector machine (LS-SVM) regression was applied for relating the concentrations of both compounds to their CL profiles. The parameters of the model consisting of σ2 and γ were optimized by constructing LS-SVM models with all possible combinations of these two parameters to select the model with the minimum root mean squared error of cross validation (RMSECV) as the best. The parameters of this model were then selected as optimized values. Under the optimized experimental conditions for both compounds, the detection limits obtained using the LS-SVM regression were 0.08 and 0.1 μmol L -1 for noscapine and thebaine, respectively. The proposed method was utilized for the simultaneous determination of the compounds in pharmaceutical formulations and plasma samples with satisfactory results.
Sforza, Federico
2014-01-01
This paper describes an innovative way to optimize a multivariate classifier, in particular a Support Vector Machine algorithm, on a problem characterized by a biased training sample. This is possible thanks to the feedback of a signal-background template fit performed on a validation sample and included both in the optimization process and in the input variable selection. The procedure is applied to a real case of interest at hadron collider experiments: the reduction and the estimate of the multi-jet background in the $W\\to e \
A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...
Directory of Open Access Journals (Sweden)
S. Johnson
2011-01-01
Full Text Available Problem statement: All compilers have simple profiling-based heuristics to identify and predict program hot methods and also to make optimization decisions. The major challenge in the profile-based optimization is addressing the problem of overhead. The aim of this work is to perform feature subset selection using Genetic Algorithms (GA to improve and refine the machine learnt static hot method predictive technique and to compare the performance of the new models against the simple heuristics. Approach: The relevant features for training the predictive models are extracted from an initial set of randomly selected ninety static program features, with the help of the GA wrapped with the predictive model using the Support Vector Machine (SVM, a Machine Learning (ML algorithm. Results: The GA-generated feature subsets containing thirty and twenty nine features respectively for the two predictive models when tested on MiBench predict Long Running Hot Methods (LRHM and frequently called hot methods (FCHM with the respective accuracies of 71% and 80% achieving an increase of 19% and 22%. Further, inlining of the predicted LRHM and FCHM improve the program performance by 3% and 5% as against 4% and 6% with Low Level Virtual Machines (LLVM default heuristics. When intra-procedural optimizations (IPO are performed on the predicted hot methods, this system offers a performance improvement of 5% and 4% as against 0% and 3% by LLVM default heuristics on LRHM and FCHM respectively. However, we observe an improvement of 36% in certain individual programs. Conclusion: Overall, the results indicate that the GA wrapped with SVM derived feature reduction improves the hot method prediction accuracy and that the technique of hot method prediction based optimization is potentially useful in selective optimization.
A three-stage expert system based on support vector machines for thyroid disease diagnosis.
Chen, Hui-Ling; Yang, Bo; Wang, Gang; Liu, Jie; Chen, Yi-Dong; Liu, Da-You
2012-06-01
In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
Directory of Open Access Journals (Sweden)
Wenliao Du
2013-01-01
Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.
Trajectory optimization for vehicles using control vector parameterization and nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Spangelo, I.
1994-12-31
This thesis contains a study of optimal trajectories for vehicles. Highly constrained nonlinear optimal control problems have been solved numerically using control vector parameterization and nonlinear programming. Control vector parameterization with shooting has been described in detail to provide the reader with the theoretical background for the methods which have been implemented, and which are not available in standard text books. Theoretical contributions on accuracy analysis and gradient computations have also been presented. Optimal trajectories have been computed for underwater vehicles controlled in all six degrees of freedom by DC-motor driven thrusters. A class of nonlinear optimal control problems including energy-minimization, possibly combined with time minimization and obstacle avoidance, has been developed. A program system has been specially designed and written in the C language to solve this class of optimal control problems. Control vector parameterization with single shooting was used. This special implementation has made it possible to perform a detailed analysis, and to investigate numerical details of this class of optimization methods which would have been difficult using a general purpose CVP program system. The results show that this method for solving general optimal control problems is well suited for use in guidance and control of marine vehicles. Results from rocket trajectory optimization has been studied in this work to bring knowledge from this area into the new area of trajectory optimization of marine vehicles. 116 refs., 24 figs., 23 tabs.
DNA regulatory motif selection based on support vector machine ...
African Journals Online (AJOL)
Administrator
2011-10-19
Oct 19, 2011 ... we suggested that the methods used could be applied to other microarray experiments to explore .... But these factors were not included in this sample model. ... SVM draws an optimal hyperplane in a high-dimensional feature.
Scaling Support Vector Machines On Modern HPC Platforms
Energy Technology Data Exchange (ETDEWEB)
You, Yang; Fu, Haohuan; Song, Shuaiwen; Randles, Amanda; Kerbyson, Darren J.; Marquez, Andres; Yang, Guangwen; Hoisie, Adolfy
2015-02-01
We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.
Institute of Scientific and Technical Information of China (English)
徐强; 刘永前; 田德; 张晋华; 龙泉
2014-01-01
滚动轴承故障诊断是提高设备利用率、降低运行及维护成本关键。最小二乘支持向量回归机为有效的故障诊断方法，为解决其参数选取受限于主观经验问题，将萤火虫群算法用于惩罚系数C与核参数σ寻优，提出基于萤火虫群算法优化最小二乘支持向量回归机的滚动轴承故障诊断方法。实验结果表明，该方法能对滚动轴承故障位置及程度进行准确诊断，与常规最小二乘支持向量回归机、BP神经网络相比精度更高，由此验证该方法的可靠性。%Fault diagnosis of rolling bearings is the key to improve equipment availability and reduce operation and maintenance cost.Least square support vector regression (LSSVR)is an effective method for fault diagnosis.Here,the glowworm swarm optimization (GSO)algorithm was applied to search the optimal combination of penalty and kernel parameters often restricted by subjective experience in LSSVR.A rolling bearing fault diagnosis method using LSSVR based on GSO was proposed.Tests showed that the presented method can be used to precisely diagnose both fault location and fault severity of rolling bearings,it has a higher accuracy compared with the normal LSSVR and BP neural network, so the reliability of the proposed method is verified.
Yan, Hua-Wen; Huang, Xiao-Lin; Zhao, Ying; Si, Jun-Feng; Liu, Tie-Bing; Liu, Hong-Xing
2014-11-01
A series of experiments are conducted to confirm whether the vectors calculated for an early section of a continuous non-invasive fetal electrocardiogram (fECG) recording can be directly applied to subsequent sections in order to reduce the computation required for real-time monitoring. Our results suggest that it is generally feasible to apply the initial optimal maternal and fetal ECG combination vectors to extract the fECG and maternal ECG in subsequent recorded sections.
A support vector machine approach to detect financial statement fraud in South Africa: A first look
CSIR Research Space (South Africa)
Moepya, SO
2014-04-01
Full Text Available Auditors face the difficult task of detecting companies that issue manipulated financial statements. In recent years, machine learning methods have provided a feasible solution to this task. This study develops support vector machine (SVM) models...
CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification,using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very effective.
Institute of Scientific and Technical Information of China (English)
Dong Qiwen; Wang Xiaolong; Lin Lei
2007-01-01
Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. A novel method for domain boundary prediction has been presented, which combines the support vector machine with domain guess by size algorithm. Since the evolutional information of multiple domains can be detected by position specific score matrix, the support vector machine method is trained and tested using the values of position specific score matrix generated by PSI-BLAST. The candidate domain boundaries are selected from the output of support vector machine, and are then inputted to domain guess by size algorithm to give the final results of domain boundary prediction. The experimental results show that the combined method outperforms the individual method of both support vector machine and domain guess by size.
Mass detection algorithm based on support vector machine and relevance feedback
Institute of Scientific and Technical Information of China (English)
Ying WANG; Xinbo GAO
2008-01-01
To improve the detection of mass with appearance that borders on the similarity between mass and density tissues in the breast,an support vector machine classifier based on typical features iS designed to classify the region of interest(ROI).Furthermore,relevance feedback is introduced to improve the performance of support vector machines.A new mass detection scheme based on the support vector machine and the relevance feedback is proposed.Simulation experiments on mammograms illustrate that the novel support vector machine classifier based on typical features can improve the detection performance of the featureless classifier by 5%,while the introduction of relevance feedback can further improve the detection performance to about 90%.
Indah Agustien; Muhammad Rahmat Widyanto; Sukmawati Endah; Tarzan Basaruddin
2010-01-01
Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine is proposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonized using thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose, whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce t...
Institute of Scientific and Technical Information of China (English)
WuXiaojun; YangJingyu; JosefKittler; WangShitong; LiuTongming; KieronMesser
2004-01-01
A study has been made on the essence of optimal uncorrelated discriminant vectors. A whitening transform has been constructed by means of the eigen decomposition of the population scatter matrix, which makes the population scatter matrix be an identity matrix in the transformed sample space no matter whether the population scatter matrix is singular or not. Thus, the optimal discriminant vectors solved by the conventional linear discriminant analysis (LDA) methods are statistically uncorrelated. The research indicates that the essence of the statistically uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristics of the proposed method is that the obtained optimal discriminant vectors are not only orthogonal but also statistically uneorrelated. The proposed method is applicable to all the problems of algebraic feature extraction. The numerical experiments on several facial databases show the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Phan Quoc Khanh
2014-01-01
Full Text Available The purpose of this paper is introduce several types of Levitin-Polyak well-posedness for bilevel vector equilibrium and optimization problems with equilibrium constraints. Base on criterion and characterizations for these types of Levitin-Polyak well-posedness we argue on diameters and Kuratowski’s, Hausdorff’s, or Istrǎtescus measures of noncompactness of approximate solution sets under suitable conditions, and we prove the Levitin-Polyak well-posedness for bilevel vector equilibrium and optimization problems with equilibrium constraints. Obtain a gap function for bilevel vector equilibrium problems with equilibrium constraints using the nonlinear scalarization function and consider relations between these types of LP well-posedness for bilevel vector optimization problems with equilibrium constraints and these types of Levitin-Polyak well-posedness for bilevel vector equilibrium problems with equilibrium constraints under suitable conditions; we prove the Levitin-Polyak well-posedness for bilevel equilibrium and optimization problems with equilibrium constraints.
Lu, Zhao; Sun, Jing; Butts, Kenneth
2014-05-01
Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.
Chaotic time series prediction using mean-field theory for support vector machine
Institute of Scientific and Technical Information of China (English)
Cui Wan-Zhao; Zhu Chang-Chun; Bao Wen-Xing; Liu Jun-Hua
2005-01-01
This paper presents a novel method for predicting chaotic time series which is based on the support vector machines approach, and it uses the mean-field theory for developing an easy and efficient learning procedure for the support vector machine. The proposed method approximates the distribution of the support vector machine parameters to a Gaussian process and uses the mean-field theory to estimate these parameters easily, and select the weights of the mixture of kernels used in the support vector machine estimation more accurately and faster than traditional quadratic programming-based algorithms. Finally, relationships between the embedding dimension and the predicting performance of this method are discussed, and the Mackey-Glass equation is applied to test this method. The stimulations show that the mean-field theory for support vector machine can predict chaotic time series accurately, and even if the embedding dimension is unknown, the predicted results are still satisfactory. This result implies that the mean-field theory for support vector machine is a good tool for studying chaotic time series.
Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie
2014-09-01
Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique.
Institute of Scientific and Technical Information of China (English)
Xin LIU; Guo WEI; Jin-wei SUN; Dan LIU
2009-01-01
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to muhifunctional sensor signal reconstruction. For three different nonlinearities of a multi functional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.03184% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multi functional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.
Institute of Scientific and Technical Information of China (English)
赵冠华; 李玥; 赵娟
2011-01-01
When using traditional support vector machine to make financial distress prediction, we need to solve the complex quadratic programming problems, which are quite difficult. At the same time, the least squares support vector machine （LS-SVM） can solve the quadratic programming problems by transferring them into linear equations, effectively reducing the difficulty. Especially when applying genetic algorithm to optimize parameters and kernel parameters of LSSVM, the prediction accuracy is significantly improved. We randomly selected 252 A-share listed companies during 2002-2007 from Shanghai and Shenzhen Stock Exchanges as the research samples and divided them into two Sample I and Sample II. Then we carried out short-term and long-term predictions of these two sets of samples res The empirical results showed that the prediction effects of LS-SVM model based on genetic algorithm was better of traditional statistical Logit Model as well as the traditional support vector machine. higher accuracy rate compared with long-term prediction. groups - pectively. than that Besides, short-term prediction had a In addition, the number of training samples directly affected the prediction accuracy and they were positively correlated.%传统支持向量机应用于财务困境预测时，需要求解复杂的二次规划问题，求解难度大。而最小二乘支持向量机模型可以将二次规划问题变成一个线性方程组来求解，有效降低了模型求解的难度。尤其是将遗传算法应用于最小二乘支持向量机模型参数和核参数的优化时，显著提高了模型预测的正确率。本文从沪深两市随机抽取了2002年-2007年252家A股上市公司作为研究样本，并把研究样本分为两组，对这两组样本数据分别进行了短期及中长期预测。实证结果表明，基于遗传算法的最小二乘支持向量机模型的预测效果不但好于传统统计类Logit模型，也优于传统支持向量机模型
Khawaja, Taimoor Saleem
A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior
Variance inflation in high dimensional Support Vector Machines
DEFF Research Database (Denmark)
Abrahamsen, Trine Julie; Hansen, Lars Kai
2013-01-01
Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors...... is not the full input space. Hence, when applying the model to future data the model is effectively blind to the missed orthogonal subspace. This can lead to an inflated variance of hidden variables estimated in the training set and when the model is applied to test data we may find that the hidden variables...... follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including...
Directory of Open Access Journals (Sweden)
N. Sujay Raghavendra
2015-12-01
Full Text Available This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of both the SVR and WP–SVR models is assessed using different statistical indices. From the comparative result analysis of the developed models, it can be seen that WP–SVR model outperforms the classic SVR model in predicting groundwater levels at all the three well locations (e.g. NRMSE(WP–SVR = 7.14, NRMSE(SVR = 12.27; NSE(WP–SVR = 0.91, NSE(SVR = 0.8 during the test phase with respect to well location at Surathkal. Therefore, using the WP–SVR model is highly acceptable for modeling and forecasting of groundwater level fluctuations.
Optimization with quadratic support functions in nonconvex smooth optimization
Khamisov, O. V.
2016-10-01
Problem of global minimization of twice continuously differentiable function with Lipschitz second derivatives over a polytope is considered. We suggest a branch and bound method with polytopes as partition elements. Due to the Lipschitz property of the objective function we can construct a quadratic support minorant at each point of the feasible set. Global minimum of of this minorant provides a lower bound of the objective over given partition subset. The main advantage of the suggested method consists in the following. First quadratic minorants usually are nonconvex and we have to solve auxiliary global optimization problem. This problem is reduced to a mixed 0-1 linear programming problem and can be solved by an advanced 0-1 solver. Then we show that the quadratic minorants are getting convex as soon as partition elements are getting smaller in diameter. Hence, at the final steps of the branch and bound method we solve convex auxiliary quadratic problems. Therefore, the method accelerates when we are close to the global minimum of the initial problem.
Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines.
Kyrkou, Christos; Bouganis, Christos-Savvas; Theocharides, Theocharis; Polycarpou, Marios M
2016-01-01
Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples. The proposed SVM cascade architecture is implemented on a Spartan-6 field-programmable gate array (FPGA) platform and evaluated for object detection on 800×600 (Super Video Graphics Array) resolution images. The proposed architecture, boosted by a neural network that processes cascade information, achieves a real-time processing rate of 40 frames/s for the benchmark face detection application. Furthermore, the hardware-reduction method results in the utilization of 25% less FPGA custom-logic resources and 20% peak power reduction compared with a baseline implementation.
An UWB LNA Design with PSO Using Support Vector Microstrip Line Model
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Salih Demirel
2015-01-01
Full Text Available A rigorous and novel design procedure is constituted for an ultra-wideband (UWB low noise amplifier (LNA by exploiting the 3D electromagnetic simulator based support vector regression machine (SVRM microstrip line model. First of all, in order to design input and output matching circuits (IMC-OMC, source ZS and load ZL termination impedance of matching circuit, which are necessary to obtain required input VSWR (Vireq, noise (Freq, and gain (GTreq, are determined using performance characterisation of employed transistor, NE3512S02, between 3 and 8 GHz frequencies. After the determination of the termination impedance, to provide this impedance with IMC and OMC, dimensions of microstrip lines are obtained with simple, derivative-free, easily implemented algorithm Particle Swarm Optimization (PSO. In the optimization of matching circuits, highly accurate and fast SVRM model of microstrip line is used instead of analytical formulations. ADCH-80a is used to provide ultra-wideband RF choking in DC bias. During the design process, it is aimed that Vireq = 1.85, Freq = Fmin, and GTreq = GTmax all over operating frequency band. Measurements taken from the realized LNA demonstrate the success of this approximation over the band.
Mask optimization approaches in optical lithography based on a vector imaging model.
Ma, Xu; Li, Yanqiu; Dong, Lisong
2012-07-01
Recently, a set of gradient-based optical proximity correction (OPC) and phase-shifting mask (PSM) optimization methods has been developed to solve for the inverse lithography problem under scalar imaging models, which are only accurate for numerical apertures (NAs) of less than approximately 0.4. However, as lithography technology enters the 45 nm realm, immersion lithography systems with hyper-NA (NA>1) are now extensively used in the semiconductor industry. For the hyper-NA lithography systems, the vector nature of the electromagnetic field must be taken into account, leading to the vector imaging models. Thus, the OPC and PSM optimization approaches developed under the scalar imaging models are inadequate to enhance the resolution in immersion lithography systems. This paper focuses on developing pixelated gradient-based OPC and PSM optimization algorithms under a vector imaging model. We first formulate the mask optimization framework, in which the imaging process of the optical lithography system is represented by an integrative and analytic vector imaging model. A gradient-based algorithm is then used to optimize the mask iteratively. Subsequently, a generalized wavelet penalty is proposed to keep a balance between the mask complexity and convergence errors. Finally, a set of methods is exploited to speed up the proposed algorithms.
Institute of Scientific and Technical Information of China (English)
邓刚; 许杭琳; 焦聪聪; 朱程华
2012-01-01
采用了基于统计学习理论的支持向量机(SVM)回归方法对佛手精油β-环糊精包含物制备的工艺条件进行了预测及优化.通过正交试验为构建SVM回归模型提供了基础数据,并运用交叉验证对模型参数进行了优化.回归结果显示,回收率SVM模型(MSE=0.003,R2=0.9581)和包埋率SVM模型(MSE=0.007,R2=0.9008)得到的回归预测值和试验测定值拟合良好,两者相对误差小于0.1％的回归值比例分别为100％和88.0％,表明SVM回归法可精确地预测挥发油β-环糊精包含物制备工艺状况,从而获取更为可靠的最优化工艺条件.%Optimization of microcapsule preparation technology of bergamot essential oil and (3 - cyclodextrin based on support vector machines was studied in the paper. The SVM prediction model was constructed depending on orthogonal experimental data, which parameters were optimized by cross -validation statistical method. SVM analysis results showed that the data percentage of relative error below 0. 1 % of product yield - SVM model ( MSE = 0. 003, R2 =0.958 l)and embedding rate - SVM model(MSE =0.007,R2 =0.900 8)are 100.00% and 88.00% ,respectively. The SVM prediction agreed so well with the experimental data that constructed SVM regression model could predict microcapsule preparation process accurately, resulting in more reliable optimal production.
Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression.
Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Abdul Wahab, Ahmad Khairi; Hasnan, Nazirah; Olatunji, Sunday Olusanya; Davis, Glen M
2016-07-19
The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar
Directory of Open Access Journals (Sweden)
Nicoletta Roberto
2017-07-01
Full Text Available An algorithm based on a support vector machine (SVM is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.
Prediction of protein binding sites in protein structures using hidden Markov support vector machine
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Lin Lei
2009-11-01
Full Text Available Abstract Background Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance. Results In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods. Conclusion The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.
Matasci, G.; Pozdnoukhov, A.; Kanevski, M.
2009-04-01
The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the
Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting.
Li, Youyuan; Hao, Pei; Zhang, Siliang; Li, Yixue
2011-12-01
Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly
Recognition and classification of histones using support vector machine
Bhasin, Manoj; Reinherz, Ellis L.; Reche, Pedro A.
2006-01-01
Histones are DNA-binding proteins found in the chromatin of all eukaryotic cells. They are highly conserved and can be grouped into five major classes: H1/H5, H2A, H2B, H3, and H4. Two copies of H2A, H2B, H3, and H4 bind to about 160 base pairs of DNA forming the core of the nucleosome (the repeating structure of chromatin) and H1/H5 bind to its DNA linker sequence. Overall, histones have a high arginine/lysine content that is optimal for interaction with DNA. This sequence bias can make the ...
Fuel-optimal angular momentum vector control for spinning and dual-spin spacecraft.
Larson, V.; Likins, P.
1973-01-01
The problem of fuel-optimal small-angle reorientation of the spin axis of a spinning or dual-spin spacecraft is examined. The results obtained show significant improvements over previously published optimization studies by virtue of the introduction of two innovations: (1) mass-explusion active control is utilized for angular momentum vector pointing only, with passive damping relied upon for stable vehicles to attenuate vehicle coning about the angular momentum vector, so that the task of the active controller changes from spin axis control to angular momentum vector control, and (2) several options are considered for type, number, and location of attitude control jets. The first of these considerations introduces a target set which is a smooth, two-dimensional linear manifold in the four-dimensional state space, whereas previous studies have adopted the origin as the target set. The second innovation amounts to consideration of a spectrum of control restraint sets.
Carbon dioxide emission prediction using support vector machine
Saleh, Chairul; Rachman Dzakiyullah, Nur; Bayu Nugroho, Jonathan
2016-02-01
In this paper, the SVM model was proposed for predict expenditure of carbon (CO2) emission. The energy consumption such as electrical energy and burning coal is input variable that affect directly increasing of CO2 emissions were conducted to built the model. Our objective is to monitor the CO2 emission based on the electrical energy and burning coal used from the production process. The data electrical energy and burning coal used were obtained from Alcohol Industry in order to training and testing the models. It divided by cross-validation technique into 90% of training data and 10% of testing data. To find the optimal parameters of SVM model was used the trial and error approach on the experiment by adjusting C parameters and Epsilon. The result shows that the SVM model has an optimal parameter on C parameters 0.1 and 0 Epsilon. To measure the error of the model by using Root Mean Square Error (RMSE) with error value as 0.004. The smallest error of the model represents more accurately prediction. As a practice, this paper was contributing for an executive manager in making the effective decision for the business operation were monitoring expenditure of CO2 emission.
Convex Array Vector Velocity Imaging Using Transverse Oscillation and Its Optimization
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Brandt, Andreas Hjelm; Bachmann Nielsen, Michael
2015-01-01
A method for obtaining vector flow images using the transverse oscillation (TO) approach on a convex array is presented. The paper presents optimization schemes for TO fields and evaluates their performance using simulations and measurements with an experimental scanner. A 3-MHz 192-element conve...
A path algorithm for the support vector domain description and its application to medical imaging
DEFF Research Database (Denmark)
Sjöstrand, Karl; Hansen, Michael Sass; Larsson, Henrik B. W.
2007-01-01
The support vector domain description is a one-class classification method that estimates the distributional support of a data set. A flexible closed boundary function is used to separate trustworthy data on the inside from outliers on the outside. A single regularization parameter determines the...
Strategic Bidding for Electri city Markets Negotiation Using Support Vector Machines
DEFF Research Database (Denmark)
Pereira, Rafael; Sousa, Tiago; Pinto, Tiago
2014-01-01
. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...
Object Recognition System-on-Chip Using the Support Vector Machines
Directory of Open Access Journals (Sweden)
Houzet Dominique
2005-01-01
Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.
Directory of Open Access Journals (Sweden)
Wang Lily
2008-07-01
Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
Institute of Scientific and Technical Information of China (English)
高昆仑; 刘建明; 徐茹枝; 王宇飞; 李怡康
2011-01-01
提出一种基于支持向量机和粒子群算法的网络态势复合预测模型.模型使用滑动窗口方法将各原始离散时间监测点的安全态势值构造成部分线性相关的连续时间序列,以其作为安全态势数据样本集对支持向量机加以训练,生成预测模型.在支持向量机训练过程中,利用粒子群算法搜寻支持向量机的最优训练参数,以降低支持向量机参数选择的盲目性,提高预测精度.最后通过基于大量电力企业信息网络现场安全监测数据的实验,验证了复合预测模型的有效性.%A security situation prediction model for information network based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed. By use of sliding window, in the proposed model a continuous time series that is partially linearly dependent is constructed by security situation values sampled from original discrete time monitoring points, and taking the time series as the sample set of security situation data the SVM is trained to generate a prediction model. During the training of SVM, the PSO algorithm is used to search for the optimal training parameters of SVM to reduce the blindness in the selection of SVM parameters and improve precision of prediction.Through the experiments based on on-site installation and monitoring data of a lot of power enterprise information networks, the effectiveness of the proposed security situation prediction model is verified.
Terzic, Jenny; Nagarajah, Romesh; Alamgir, Muhammad
2013-01-01
Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which has far greater accuracy than conventional signal averaging methods. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach describes the research and development of a fluid level measurement system for dynamic environments. The measurement system is based on a single ultrasonic sensor. A Support Vector Machines (SVM) based signal characterization and processing system has been developed to compensate for the effects of slosh and temperature variation in fluid level measurement systems used in dynamic environments including automotive applications. It has been demonstrated that a simple ν-SVM model with Radial Basis Function (RBF) Kernel with the inclusion of a Moving Median filter could be used to achieve the high levels...
Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine.
Wang, Ya-hui; Liu, Tai-ang; Wei, Hua; Wan, Lei; Ying, Chong-liang; Zhu, Guang-you
2016-03-01
The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (PA ). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.
Support vector machine method for forecasting future strong earthquakes in Chinese mainland
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization (forecasting) ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world,however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.
A Support Vector Machine-Based Dynamic Network for Visual Speech Recognition Applications
Directory of Open Access Journals (Sweden)
Mihaela Gordan
2002-11-01
Full Text Available Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates.
Application of support vector machine in the prediction of mechanical property of steel materials
Institute of Scientific and Technical Information of China (English)
Ling Wang; Zhichun Mu; Hui Guo
2006-01-01
The investigation of the influences of important parameters including steel chemical composition and hot rolling parameters on the mechanical properties of steel is a key for the systems that are used to predict mechanical properties. To improve the prediction accuracy, support vector machine was used to predict the mechanical properties of hot-rolled plain carbon steel Q235B. Support vector machine is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample, nonlinearity, and high dimension with a good generalization performance. On the basis of the data collected from the supervisor of hotrolling process, the support vector regression algorithm was used to build prediction models, and the off-line simulation indicates that predicted and measured results are in good agreement.
DDoS detection based on wavelet kernel support vector machine
Institute of Scientific and Technical Information of China (English)
YANG Ming-hui; WANG Ru-chuan
2008-01-01
To enhance the detection accuracy and deduce false positive rate of distributed denial of service (DDoS) attack detection, a new machine learning method was proposed. With the analysis of support vector machine (SVM) and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine (WSVM) is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.
Blagrove, Marcus S C; Caminade, Cyril; Waldmann, Elisabeth; Sutton, Elizabeth R; Wardeh, Maya; Baylis, Matthew
2017-06-01
Mosquito-borne viruses have been estimated to cause over 100 million cases of human disease annually. Many methodologies have been developed to help identify areas most at risk from transmission of these viruses. However, generally, these methodologies focus predominantly on the effects of climate on either the vectors or the pathogens they spread, and do not consider the dynamic interaction between the optimal conditions for both vector and virus. Here, we use a new approach that considers the complex interplay between the optimal temperature for virus transmission, and the optimal climate for the mosquito vectors. Using published geolocated data we identified temperature and rainfall ranges in which a number of mosquito vectors have been observed to co-occur with West Nile virus, dengue virus or chikungunya virus. We then investigated whether the optimal climate for co-occurrence of vector and virus varies between "warmer" and "cooler" adapted vectors for the same virus. We found that different mosquito vectors co-occur with the same virus at different temperatures, despite significant overlap in vector temperature ranges. Specifically, we found that co-occurrence correlates with the optimal climatic conditions for the respective vector; cooler-adapted mosquitoes tend to co-occur with the same virus in cooler conditions than their warmer-adapted counterparts. We conclude that mosquitoes appear to be most able to transmit virus in the mosquitoes' optimal climate range, and hypothesise that this may be due to proportionally over-extended vector longevity, and other increased fitness attributes, within this optimal range. These results suggest that the threat posed by vector-competent mosquito species indigenous to temperate regions may have been underestimated, whilst the threat arising from invasive tropical vectors moving to cooler temperate regions may be overestimated.
Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era
Energy Technology Data Exchange (ETDEWEB)
Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; Balaprakash, Prasanna; Unsal, Osman; Labarta, Jesus; Cristal, Adrian; Cappello, Franck
2016-01-01
As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.
Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images.
Zhang, Jianfeng; Xu, Jiatuo; Hu, Xiaojuan; Chen, Qingguang; Tu, Liping; Huang, Jingbin; Cui, Ji
2017-01-01
Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.
Directory of Open Access Journals (Sweden)
Seyyid Ahmed Medjahed
2016-12-01
Full Text Available Support vector machine (SVM is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM and Threshold Accepting (TA-SVM to determine the optimal value parameter and the relevant features subset, without reducing SVM classification accuracy. In order to evaluate the performance of MA-SVM and TA-SVM, several public datasets are employed to compute the classification accuracy rate. The proposed approaches were tested in the context of medical diagnosis. Also, we tested the approaches on DNA microarray datasets used for cancer diagnosis. The results obtained by the MA-SVM and TA-SVM algorithms are shown to be superior and have given a good performance in the DNA microarray data sets which are characterized by the large number of features. Therefore, the MA-SVM and TA-SVM approaches are well suited for parameter determination and feature selection in SVM.
Huang, Mengmeng; Wang, Qiao; Chen, Xinyu; Zhang, Yu
2017-04-15
This study investigated the effect of flavanols and their derivatives on acrylamide formation under low-moisture conditions via prediction using the support vector regression (SVR) approach. Acrylamide was generated in a potato-based equimolar asparagine-reducing sugar model system through oven heating. Both positive and negative effects were observed when the flavonoid treatment ranged 1-10,000μmol/L. Flavanols and derivatives (100μmol/L) suppress the acrylamide formation within a range of 59.9-78.2%, while their maximal promotion effects ranged from 2.15-fold to 2.84-fold for the control at a concentration of 10,000μmol/L. The correlations between inhibition rates and changes in Trolox-equivalent antioxidant capacity (ΔTEAC) (RTEAC-DPPH=0.878, RTEAC-ABTS=0.882, RTEAC-FRAP=0.871) were better than promotion rates (RTEAC-DPPH=0.815, RTEAC-ABTS=0.749, RTEAC-FRAP=0.841). Using ΔTEAC as variables, an optimized SVR model could robustly serve as a new predictive tool for estimating the effect (R: 0.783-0.880), the fitting performance of which was slightly better than that of multiple linear regression model (R: 0.754-0.880). Copyright © 2016 Elsevier Ltd. All rights reserved.
Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images
Hu, Xiaojuan; Chen, Qingguang; Tu, Liping; Huang, Jingbin; Cui, Ji
2017-01-01
Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis. PMID:28133611
[Hyperspectral image classification based on 3-D gabor filter and support vector machines].
Feng, Xiao; Xiao, Peng-feng; Li, Qi; Liu, Xiao-xi; Wu, Xiao-cui
2014-08-01
A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image.
Predictive modeling of human operator cognitive state via sparse and robust support vector machines.
Zhang, Jian-Hua; Qin, Pan-Pan; Raisch, Jörg; Wang, Ru-Bin
2013-10-01
The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human-machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.
Inferring the location of buried UXO using a support vector machine
Fernández, Juan Pablo; Sun, Keli; Barrowes, Benjamin; O'Neill, Kevin; Shamatava, Irma; Shubitidze, Fridon; Paulsen, Keith D.
2007-04-01
The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.
Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier.
Al-Angari, Haitham M; Sahakian, Alan V
2012-05-01
Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.
Directory of Open Access Journals (Sweden)
Abbas TAATI
2015-08-01
Full Text Available Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing land use maps. The aim of this study is to create land use classiﬁcation using a support vector machine (SVM and maximum likelihood classifier (MLC in Qazvin, Iran, by TM images of the Landsat 5 satellite. In the pre-processing stage, the necessary corrections were applied to the images. In order to evaluate the accuracy of the 2 algorithms, the overall accuracy and kappa coefficient were used. The evaluation results verified that the SVM algorithm with an overall accuracy of 86.67 % and a kappa coefficient of 0.82 has a higher accuracy than the MLC algorithm in land use mapping. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.
Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection
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Tian Wang
2013-12-01
Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.
Institute of Scientific and Technical Information of China (English)
Fan Youping; Chen Yunping; Sun Wansheng; Li Yu
2005-01-01
As a new type of learning machine developed on the basis of statistics learning theory, support vector machine (SVM) plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifier. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM(LS-SVM) and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-classification is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and two-spirals to measure the performance of the classifier.
Lima, Clodoaldo A M; Coelho, André L V; Eisencraft, Marcio
2010-08-01
The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset. Copyright 2010 Elsevier Ltd. All rights reserved.
Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images
Directory of Open Access Journals (Sweden)
Jianfeng Zhang
2017-01-01
Full Text Available Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM. Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA, while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA, the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM is of great value, indicating the feasibility of digitalized tongue diagnosis.
Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting
Directory of Open Access Journals (Sweden)
Cheng-Wen Lee
2016-10-01
Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.
Active Learning for Transductive Support Vector Machines with Applications to Text Classification
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
This paper presents a novel active learning approach for transductive support vector machines with applications to text classification. The concept of the centroid of the support vectors is proposed so that the selective sampling based on measuring the distance from the unlabeled samples to the centroid is feasible and simple to compute. With additional hypothesis, active learning offers better performance with comparison to regular inductive SVMs and transductive SVMs with random sampling,and it is even competitive to transductive SVMs on all available training data. Experimental results prove that our approach is efficient and easy to implement.
Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.
Gear Fault Diagnosis Based on Rough Set and Support Vector Machine
Institute of Scientific and Technical Information of China (English)
TIAN Huifang; SUN Shanxia
2006-01-01
By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.
A support vector machine approach for classification of welding defects from ultrasonic signals
Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming
2014-07-01
Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.
Modeling personalized head-related impulse response using support vector regression
Institute of Scientific and Technical Information of China (English)
HUANG Qing-hua; FANG Yong
2009-01-01
A new customization approach based on support vector regression (SVR) is proposed to obtain individual headrelated impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression,better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR algorithm.
Kawase, Hiroshi; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi
2016-02-01
An effective solution to the continuous Internet traffic expansion is to offload traffic to lower layers such as the L2 or L1 optical layers. One possible approach is to introduce dynamic optical path operations such as adaptive establishment/tear down according to traffic variation. Path operations cannot be done instantaneously; hence, traffic prediction is essential. Conventional prediction techniques need optimal parameter values to be determined in advance by averaging long-term variations from the past. However, this does not allow adaptation to the ever-changing short-term variations expected to be common in future networks. In this paper, we propose a real-time optical path control method based on a machinelearning technique involving support vector machines (SVMs). A SVM learns the most recent traffic characteristics, and so enables better adaptation to temporal traffic variations than conventional techniques. The difficulty lies in determining how to minimize the time gap between optical path operation and buffer management at the originating points of those paths. The gap makes the required learning data set enormous and the learning process costly. To resolve the problem, we propose the adoption of multiple SVMs running in parallel, trained with non-overlapping subsets of the original data set. The maximum value of the outputs of these SVMs will be the estimated number of necessary paths. Numerical experiments prove that our proposed method outperforms a conventional prediction method, the autoregressive moving average method with optimal parameter values determined by Akaike's information criterion, and reduces the packet-loss ratio by up to 98%.
Tool Support for Software Lookup Table Optimization
Directory of Open Access Journals (Sweden)
Chris Wilcox
2011-01-01
Full Text Available A number of scientific applications are performance-limited by expressions that repeatedly call costly elementary functions. Lookup table (LUT optimization accelerates the evaluation of such functions by reusing previously computed results. LUT methods can speed up applications that tolerate an approximation of function results, thereby achieving a high level of fuzzy reuse. One problem with LUT optimization is the difficulty of controlling the tradeoff between performance and accuracy. The current practice of manual LUT optimization adds programming effort by requiring extensive experimentation to make this tradeoff, and such hand tuning can obfuscate algorithms. In this paper we describe a methodology and tool implementation to improve the application of software LUT optimization. Our Mesa tool implements source-to-source transformations for C or C++ code to automate the tedious and error-prone aspects of LUT generation such as domain profiling, error analysis, and code generation. We evaluate Mesa with five scientific applications. Our results show a performance improvement of 3.0× and 6.9× for two molecular biology algorithms, 1.4× for a molecular dynamics program, 2.1× to 2.8× for a neural network application, and 4.6× for a hydrology calculation. We find that Mesa enables LUT optimization with more control over accuracy and less effort than manual approaches.
Support Vector Machine Based Red Palm Weevil (Rynchophorus Ferrugineous, Olivier Recognition System
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Ghulam M. Hassan
2012-01-01
Full Text Available Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma Ï while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for Ï value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of Ï for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed systems success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having Ï value of 10 is optimal in identifying RPW from an image. The optimal input
Institute of Scientific and Technical Information of China (English)
SONG Qiang; WANG Ai-min
2009-01-01
The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.
Directory of Open Access Journals (Sweden)
Hehua Jiao
2014-01-01
Full Text Available In this paper, a new class of semilocal E-preinvex and related maps in Banach spaces is introduced for a nondiﬀerentiable vector optimization problem with restrictions of inequalities and some of its basic properties are studied. Furthermore, as its applications, some optimality conditions and duality results are established for a nondiﬀerentiable vector optimization under the aforesaid maps assumptions.
Directory of Open Access Journals (Sweden)
Shokri Saeid
2015-01-01
Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.
Directory of Open Access Journals (Sweden)
Co Jan Miles
2016-01-01
Full Text Available Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN, Adamic-Adar (AA, Jaccard’s Coefficient (JC, Preferential Attachment (PA, and Research Allocation Index (RA. A DBLP dataset covering the years from 2003 until 2013 was collected and transformed into time-sliced graph representations. The appropriate matrices were computed from these graphs, mapped to the feature space, and then used to build baseline VAR models with lag of 2 and some corresponding SVM classifiers. Using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC as the main fitness metric, the average result of 82.04% for the VAR was improved to 84.78% with SVM. Additional experiments to handle the highly imbalanced dataset by oversampling with SMOTE and undersampling with K-means clusters, however, did not improve the average AUC-ROC of the baseline SVM.
Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression
Directory of Open Access Journals (Sweden)
Morufu Olusola Ibitoye
2016-07-01
Full Text Available The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70% and testing (30% subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2 between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
Directory of Open Access Journals (Sweden)
Lukman Hakim
2016-02-01
Full Text Available Abstrak Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity
DEFF Research Database (Denmark)
Vlachogiannis, Ioannis (John); Lee, K Y
2009-01-01
In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...... of steady-state of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem and solved using the parallel VEPSO algorithm. The results on the IEEE 30-bus test system are compared with those given by another multi-objective evolutionary technique...... demonstrating the advantage of parallel VEPSO. The parallel VEPSO is also tested on a larger power system this with 136 busses. (C) 2009 Elsevier Ltd. All rights reserved....
SVM-Maj: a majorization approach to linear support vector machines with different hinge errors
P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi); J.C. Bioch (Cor)
2007-01-01
textabstractSupport vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal suppor
One Class Classification for Anomaly Detection: Support Vector Data Description Revisited
Pauwels, E.J.; Ambekar, O.; Perner, P.
2011-01-01
The Support Vector Data Description (SVDD) has been introduced to address the problem of anomaly (or outlier) detection. It essentially fits the smallest possible sphere around the given data points, allowing some points to be excluded as outliers. Whether or not a point is excluded, is governed by
Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates
Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...
LENUS (Irish Health Repository)
Mourao-Miranda, J
2012-05-01
To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.
Estimation of the wind turbine yaw error by support vector machines
DEFF Research Database (Denmark)
Sheibat-Othman, Nida; Othman, Sami; Tayari, Raoaa
2015-01-01
Wind turbine yaw error information is of high importance in controlling wind turbine power and structural load. Normally used wind vanes are imprecise. In this work, the estimation of yaw error in wind turbines is studied using support vector machines for regression (SVR). As the methodology...
Alcohols' Classification by Infrared Spectra Segment Based on Support Vector Machines
Institute of Scientific and Technical Information of China (English)
Wei XIE; Fu Sheng NIE; Meng Long LI; Guang Ming LI; Min Chun LU
2006-01-01
This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines (SVMs). The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.
DEFF Research Database (Denmark)
Meng, Anders; Shawe-Taylor, John
2005-01-01
autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product...
Swethalakshmi, H.; Jayaraman, Anitha; Chakravarthy, V. Srinivasa; Sekhar, C. Chandra
2006-01-01
http://www.suvisoft.com; A system for recognition of online handwritten characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classied. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Devanagari and Telugu scripts.
Directory of Open Access Journals (Sweden)
V. Malathi
2007-01-01
Full Text Available This study presents a novel technique based on Support Vector Machine (SVM for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena in power transformer.
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
Zhang, Tong
2001-01-01
This book is an introduction to support vector machines and related kernel methods in supervised learning, whose task is to estimate an input-output functional relationship from a training set of examples. A learning problem is referred to as classification if its output take discrete values in a set of possible categories and regression if it has continuous real-valued output.
A divide-and-combine method for large scale nonparallel support vector machines.
Tian, Yingjie; Ju, Xuchan; Shi, Yong
2016-03-01
Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming.
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Yan Hong Chen
2016-01-01
Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
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Haifeng Gao
2015-04-01
Full Text Available This research article analyzes the resonant reliability at the rotating speed of 6150.0 r/min for low-pressure compressor rotor blade. The aim is to improve the computational efficiency of reliability analysis. This study applies least squares support vector machine to predict the natural frequencies of the low-pressure compressor rotor blade considered. To build a more stable and reliable least squares support vector machine model, leave-one-out cross-validation is introduced to search for the optimal parameters of least squares support vector machine. Least squares support vector machine with leave-one-out cross-validation is presented to analyze the resonant reliability. Additionally, the modal analysis at the rotating speed of 6150.0 r/min for the rotor blade is considered as a tandem system to simplify the analysis and design process, and the randomness of influence factors on frequencies, such as material properties, structural dimension, and operating condition, is taken into consideration. Back-propagation neural network is compared to verify the proposed approach based on the same training and testing sets as least squares support vector machine with leave-one-out cross-validation. Finally, the statistical results prove that the proposed approach is considered to be effective and feasible and can be applied to structural reliability analysis.
Aero-engine fault diagnosis applying new fast support vector algorithm
Institute of Scientific and Technical Information of China (English)
XU Qi-hua; GENG Shuai; SHI Jun
2012-01-01
A new fast learning algorithm was presented to solve the large-scale support vector machine （ SVM ） training problem of aero-engine fault diagnosis.The relative boundary vectors （ RBVs ） instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.
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Jia Uddin
2014-01-01
Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.
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Qinghai He
2013-01-01
Full Text Available In general Banach spaces, we consider a vector optimization problem (SVOP in which the objective is a set-valued mapping whose graph is the union of finitely many polyhedra or the union of finitely many generalized polyhedra. Dropping the compactness assumption, we establish some results on structure of the weak Pareto solution set, Pareto solution set, weak Pareto optimal value set, and Pareto optimal value set of (SVOP and on connectedness of Pareto solution set and Pareto optimal value set of (SVOP. In particular, we improved and generalize, Arrow, Barankin, and Blackwell’s classical results in Euclidean spaces and Zheng and Yang’s results in general Banach spaces.
Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine
Institute of Scientific and Technical Information of China (English)
XU Rui-Rui; BIAN Guo-Xing; GAO Chen-Feng; CHEN Tian-Lun
2005-01-01
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction.First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines
Institute of Scientific and Technical Information of China (English)
Yun-Fei Wang; Huan Chen; Yan-Hong Zhou
2005-01-01
A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.
An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis
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Sandeep Chaurasia
2013-09-01
Full Text Available Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the performance measurement through bio-learning class performance while measuring AUC, specificity and sensitivity. The performance of the SVM is much better than the other machine learning classifier.
Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine
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Jian-Jiun Ding
2012-07-01
Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.
A support vector machine based test for incongruence between sets of trees in tree space
2012-01-01
Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The
Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu
2011-01-01
Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the
Armbruster, Nicole; Lattanzi, Annalisa; Jeavons, Matthieu; Van Wittenberghe, Laetitia; Gjata, Bernard; Marais, Thibaut; Martin, Samia; Vignaud, Alban; Voit, Thomas; Mavilio, Fulvio; Barkats, Martine; Buj-Bello, Ana
2016-01-01
Spinal muscular atrophy (SMA) is an autosomal recessive disease of variable severity caused by mutations in the SMN1 gene. Deficiency of the ubiquitous SMN function results in spinal cord α-motor neuron degeneration and proximal muscle weakness. Gene replacement therapy with recombinant adeno-associated viral (AAV) vectors showed therapeutic efficacy in several animal models of SMA. Here, we report a study aimed at analyzing the efficacy and biodistribution of a serotype-9, self-complementary AAV vector expressing a codon-optimized human SMN1 coding sequence (coSMN1) under the control of the constitutive phosphoglycerate kinase (PGK) promoter in neonatal SMNΔ7 mice, a severe animal model of the disease. We administered the scAAV9-coSMN1 vector in the intracerebroventricular (ICV) space in a dose-escalating mode, and analyzed survival, vector biodistribution and SMN protein expression in the spinal cord and peripheral tissues. All treated mice showed a significant, dose-dependent rescue of lifespan and growth with a median survival of 346 days. Additional administration of vector by an intravenous route (ICV+IV) did not improve survival, and vector biodistribution analysis 90 days postinjection indicated that diffusion from the cerebrospinal fluid to the periphery was sufficient to rescue the SMA phenotype. These results support the preclinical development of SMN1 gene therapy by CSF vector delivery.
Directory of Open Access Journals (Sweden)
Nicole Armbruster
2016-01-01
Full Text Available Spinal muscular atrophy (SMA is an autosomal recessive disease of variable severity caused by mutations in the SMN1 gene. Deficiency of the ubiquitous SMN function results in spinal cord α-motor neuron degeneration and proximal muscle weakness. Gene replacement therapy with recombinant adeno-associated viral (AAV vectors showed therapeutic efficacy in several animal models of SMA. Here, we report a study aimed at analyzing the efficacy and biodistribution of a serotype-9, self-complementary AAV vector expressing a codon-optimized human SMN1 coding sequence (coSMN1 under the control of the constitutive phosphoglycerate kinase (PGK promoter in neonatal SMNΔ7 mice, a severe animal model of the disease. We administered the scAAV9-coSMN1 vector in the intracerebroventricular (ICV space in a dose-escalating mode, and analyzed survival, vector biodistribution and SMN protein expression in the spinal cord and peripheral tissues. All treated mice showed a significant, dose-dependent rescue of lifespan and growth with a median survival of 346 days. Additional administration of vector by an intravenous route (ICV+IV did not improve survival, and vector biodistribution analysis 90 days postinjection indicated that diffusion from the cerebrospinal fluid to the periphery was sufficient to rescue the SMA phenotype. These results support the preclinical development of SMN1 gene therapy by CSF vector delivery.
Directory of Open Access Journals (Sweden)
Liao Li
2010-10-01
Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on
Breast cancer diagnosis using level-set statistics and support vector machines.
Liu, Jianguo; Yuan, Xiaohui; Buckles, Bill P
2008-01-01
Breast cancer diagnosis based on microscopic biopsy images and machine learning has demonstrated great promise in the past two decades. Various feature selection (or extraction) and classification algorithms have been attempted with success. However, some feature selection processes are complex and the number of features used can be quite large. We propose a new feature selection method based on level-set statistics. This procedure is simple and, when used with support vector machines (SVM), only a small number of features is needed to achieve satisfactory accuracy that is comparable to those using more sophisticated features. Therefore, the classification can be completed in much shorter time. We use multi-class support vector machines as the classification tool. Numerical results are reported to support the viability of this new procedure.
Institute of Scientific and Technical Information of China (English)
韩晓慧; 杜松怀; 苏娟; 关海鸥; 邵利敏
2014-01-01
Currently, residual current operated protective devices (RCDs) have a wide range of application in low voltage power grids and play an important role in preventing electric shock hazards and avoiding fire disasters caused by ground fault. But, the stocking current of the animals and human beings has no relationship with the setting value of action current from the protection devices, and the root mean square (RMS) value of residual current detected is considered the current value to determine if the protector acts or not. Theoretical analysis and operation experiences indicate that such criterion is unavailable in identifying the shocking current signals of the animals and human beings from the summation leakage current .Thus, in order to identify the electric shock signal from the summation leakage current automatically and accurately, intelligent information processing techniques are adopted and identification method based on least square-support vector machine (LS-SVM) with grid search and cross validation optimization are proposed. Firstly, through the experiments simulating various scenarios of rabbits electric shocking on the electric shock experiment platform of residual current operated protective devices(RCDs), signal data of 800 sample points before the one cycle and after three cycles of electric shock are used as electric shock sample data obtained by fault recorder to get the leakage current and electric shock current waveform on the electric shock process of the power supply voltage at maximum time, zero time, and any time. Secondly, the above sample data needed to be filtered to reduce the impact of the non-stationary for noise data. Then, the leakage currents of sampling points are combined into a high dimensional feature vector which is input into LS-SVM and the corresponding electric current of sampling point is employed as output of LS-SVM. The relation between input and output is trained by applying grid search and cross validation to determine
Support vector machine used to diagnose the fault of rotor broken bars of induction motors
DEFF Research Database (Denmark)
Zhitong, Cao; Jiazhong, Fang; Hongpingn, Chen
2003-01-01
The data-based machine learning is an important aspect of modern intelligent technology, while statistical learning theory (SLT) is a new tool that studies the machine learning methods in the case of a small number of samples. As a common learning method, support vector machine (SVM) is derived...... for the SVM. After a SVM is trained with learning sample vectors, so each kind of the rotor broken bar faults of induction motors can be classified. Finally the retest is demonstrated, which proves that the SVM really has preferable ability of classification. In this paper we tried applying the SVM...... from the SLT. Here we were done some analogical experiments of the rotor broken bar faults of induction motors used, analyzed the signals of the sample currents with Fourier transform, and constructed the spectrum characteristics from low frequency to high frequency used as learning sample vectors...
Watanabe, Takanori; Kessler, Daniel; Scott, Clayton; Angstadt, Michael; Sripada, Chandra
2014-08-01
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to a strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D "connectome space," offering an additional layer of interpretability that could provide new insights about various disease processes.
Wei, W. B.; Tan, L.; Jia, M. Q.; Pan, Z. K.
2017-01-01
The variational level set method is one of the main methods of image segmentation. Due to signed distance functions as level sets have to keep the nature of the functions through numerical remedy or additional technology in an evolutionary process, it is not very efficient. In this paper, a normal vector projection method for image segmentation using Chan-Vese model is proposed. An equivalent formulation of Chan-Vese model is used by taking advantage of property of binary level set functions and combining with the concept of convex relaxation. Threshold method and projection formula are applied in the implementation. It can avoid the above problems and obtain a global optimal solution. Experimental results on both synthetic and real images validate the effects of the proposed normal vector projection method, and show advantages over traditional algorithms in terms of computational efficiency.
A Decision Support System for Solving Multiple Criteria Optimization Problems
Filatovas, Ernestas; Kurasova, Olga
2011-01-01
In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria…
Directory of Open Access Journals (Sweden)
Sumi Biswas
Full Text Available BACKGROUND: Apical membrane antigen 1 (AMA1 is a leading candidate vaccine antigen against blood-stage malaria, although to date numerous clinical trials using mainly protein-in-adjuvant vaccines have shown limited success. Here we describe the pre-clinical development and optimization of recombinant human and simian adenoviral (AdHu5 and ChAd63 and orthopoxviral (MVA vectors encoding transgene inserts for Plasmodium falciparum AMA1 (PfAMA1. METHODOLOGY/PRINCIPAL FINDINGS: AdHu5-MVA prime-boost vaccination in mice and rabbits using these vectors encoding the 3D7 allele of PfAMA1 induced cellular immune responses as well as high-titer antibodies that showed growth inhibitory activity (GIA against the homologous but not heterologous parasite strains. In an effort to overcome the issues of PfAMA1 antigenic polymorphism and pre-existing immunity to AdHu5, a simian adenoviral (ChAd63 vector and MVA encoding two alleles of PfAMA1 were developed. This antigen, composed of the 3D7 and FVO alleles of PfAMA1 fused in tandem and with expression driven by a single promoter, was optimized for antigen secretion and transmembrane expression. These bi-allelic PfAMA1 vaccines, when administered to mice and rabbits, demonstrated comparable immunogenicity to the mono-allelic vaccines and purified serum IgG now showed GIA against the two divergent strains of P. falciparum encoded in the vaccine. CD8(+ and CD4(+ T cell responses against epitopes that were both common and unique to the two alleles of PfAMA1 were also measured in mice. CONCLUSIONS/SIGNIFICANCE: Optimized transgene inserts encoding two divergent alleles of the same antigen can be successfully inserted into adeno- and pox-viral vaccine vectors. Adenovirus-MVA immunization leads to the induction of T cell responses common to both alleles, as well as functional antibody responses that are effective against both of the encoded strains of P. falciparum in vitro. These data support the further clinical
Cross-Layer Optimization of MIMO-Based Mesh Networks with Gaussian Vector Broadcast Channels
Liu, Jia
2007-01-01
MIMO technology is one of the most significant advances in the past decade to increase channel capacity and has a great potential to improve network capacity for mesh networks. In a MIMO-based mesh network, the links outgoing from each node sharing the common communication spectrum can be modeled as a Gaussian vector broadcast channel. Recently, researchers showed that ``dirty paper coding'' (DPC) is the optimal transmission strategy for Gaussian vector broadcast channels. So far, there has been little study on how this fundamental result will impact the cross-layer design for MIMO-based mesh networks. To fill this gap, we consider the problem of jointly optimizing DPC power allocation in the link layer at each node and multihop/multipath routing in a MIMO-based mesh networks. It turns out that this optimization problem is a very challenging non-convex problem. To address this difficulty, we transform the original problem to an equivalent problem by exploiting the channel duality. For the transformed problem,...