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Sample records for supervised knn classifier

  1. STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery

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    Schiaffino, L.; Rosado Muñoz, A.; Guerrero Martínez, J.; Francés Villora, J.; Gutiérrez, A.; Martínez Torres, I.; Kohan, y. D. R.

    2016-04-01

    Deep Brain Stimulation (DBS) applies electric pulses into the subthalamic nucleus (STN) improving tremor and other symptoms associated to Parkinson’s disease. Accurate STN detection for proper location and implant of the stimulating electrodes is a complex task and surgeons are not always certain about final location. Signals from the STN acquired during DBS surgery are obtained with microelectrodes, having specific characteristics differing from other brain areas. Using supervised learning, a trained model based on previous microelectrode recordings (MER) can be obtained, being able to successfully classify the STN area for new MER signals. The K Nearest Neighbours (K-NN) algorithm has been successfully applied to STN detection. However, the use of the fuzzy form of the K-NN algorithm (KNN-F) has not been reported. This work compares the STN detection algorithm of K-NN and KNN-F. Real MER recordings from eight patients where previously classified by neurophysiologists, defining 15 features. Sensitivity and specificity for the classifiers are obtained, Wilcoxon signed rank non-parametric test is used as statistical hypothesis validation. We conclude that the performance of KNN-F classifier is higher than K-NN with p<0.01 in STN specificity.

  2. Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

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    Sanal Kumar, K. P.; Bhavani, R., Dr.

    2017-08-01

    Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

  3. Could k-NN Classifier be Useful in Tree Leaves Recognition?

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    Horaisová Kateřina

    2014-06-01

    Full Text Available This paper presents a method for affine invariant recognition of two-dimensional binary objects based on 2D Fourier power spectrum. Such function is translation invariant and their moments of second order enable construction of affine invariant spectrum except of the rotation effect. Harmonic analysis of samples on circular paths generates Fourier coefficients whose absolute values are affine invariant descriptors. Affine invariancy is approximately saved also for large digital binary images as demonstrated in the experimental part. The proposed method is tested on artificial data set first and consequently on a large set of 2D binary digital images of tree leaves. High dimensionality of feature vectors is reduced via the kernel PCA technique with Gaussian kernel and the k-NN classifier is used for image classification. The results are summarized as k-NN classifier sensitivity after dimensionality reduction. The resulting descriptors after dimensionality reduction are able to distinguish real contours of tree leaves with acceptable classification error. The general methodology is directly applicable to any set of large binary images. All calculations were performed in the MATLAB environment

  4. Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

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    Hussain, Hanaa M; Benkrid, Khaled; Seker, Huseyin

    2015-01-01

    Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

  5. Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier

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    Md. Kamrul Hasan

    2017-01-01

    Full Text Available Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn, standard deviation (SD, standard error (SE, modified mean absolute value (MMAV, roll-off (R, and zero crossing (ZC from the epileptic signal. The k-nearest neighbours (k-NN algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.

  6. Combining supervised classifiers with unlabeled data

    Institute of Scientific and Technical Information of China (English)

    刘雪艳; 张雪英; 李凤莲; 黄丽霞

    2016-01-01

    Ensemble learning is a wildly concerned issue. Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers. They fail to address the ensemble task where only unlabeled data are available. A label propagation based ensemble (LPBE) approach is proposed to further combine base classification results with unlabeled data. First, a graph is constructed by taking unlabeled data as vertexes, and the weights in the graph are calculated by correntropy function. Average prediction results are gained from base classifiers, and then propagated under a regularization framework and adaptively enhanced over the graph. The proposed approach is further enriched when small labeled data are available. The proposed algorithms are evaluated on several UCI benchmark data sets. Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

  7. Application of Metamorphic Testing to Supervised Classifiers

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    Xie, Xiaoyuan; Ho, Joshua; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

    2010-01-01

    Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no “test oracle” to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called “metamorphic testing”, which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well. PMID:21243103

  8. A systematic comparison of supervised classifiers.

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    Diego Raphael Amancio

    Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

  9. Combined Approach for Improving Accuracy of Prototype Selection for k-NN Classifier

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    Shikha Gadodiya

    2015-11-01

    Full Text Available The k-nearest-neighbour classifier is a powerful tool for multiclass classification and thus widely used in data mining techniques. But it consists of some severe drawbacks: high storage requirements, low noise tolerance and low efficiency in classification response. The solution to these drawbacks is to apply nearest neighbor on the reduced dataset which can be obtained by applying Prototype Selection methods on original training dataset. Various Prototype Selection methods have been developed yet but are not that efficient to overcome all the drawbacks simultaneously. So here is an attempt to build relatively more efficient algorithm by combining two or three previously developed approaches.

  10. Combined Approach for Improving Accuracy of Prototype Selection for k-NN Classifier

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    Shikha Gadodiya

    2014-05-01

    Full Text Available The k-nearest-neighbour classifier is a powerful tool for multiclass classification and thus widely used in data mining techniques. But it consists of some severe drawbacks: high storage requirements, low noise tolerance and low efficiency in classification response. The solution to these drawbacks is to apply nearest neighbor on the reduced dataset which can be obtained by applying Prototype Selection methods on original training dataset. Various Prototype Selection methods have been developed yet but are not that efficient to overcome all the drawbacks simultaneously. So here is an attempt to build relatively more efficient algorithm by combining two or three previously developed approaches.

  11. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

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    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  12. Discriminating complex networks through supervised NDR and Bayesian classifier

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    Yan, Ke-Sheng; Rong, Li-Li; Yu, Kai

    2016-12-01

    Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.

  13. Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

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    Christoff Fourie

    2014-11-01

    Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

  14. 基于修正SVM-KNN组合算法的汉语专有名词自动抽取%Automatic Extraction on Chinese Proper Names Based on a Modified SVM-KNN Classifier

    Institute of Scientific and Technical Information of China (English)

    李丽双; 党延忠; 李丹

    2011-01-01

    Extracting Chinese proper names is a key step in the fields of text mining, information retrieval and machine translation.This paper presents a method of extracting proper names from Chinese texts based on the fusion of support vector machine (SVM) and modified K nearest neighbors (KNN).Different classifiers are used for classifying the different test samples in spatial distributions.In the class phase, the algorithm computes the distance from the test sample to the hyperplane of SVM.If the distance is greater than the given threshold, the test sample would be classified on SVM;otherwise, the KNN algorithm will be used.In the practical training corpora, the negative class is represented by a large number of examples while the positive one is represented by only a few.To fit the unbalanced data, a normalized KNN classifier is proposed to modify classic KNN.The experimental results show that this model is more efficient than sole SVM and classic SVM-KNN in extracting Chinese proper names.The modified SVM-KNN model can be generalized to other fields of machine learning with unbalanced class distribution.%专有名词的自动抽取是文本挖掘、信息检索和机器翻译等领域的关键技术.本文研究了组合SVM和KNN两种分类器进行汉语专有名词自动抽取的方法.对样本在空间的不同分布使用不同的分类方法,当测试样本与SVM最优超平面的距离大于给定的阈值时使用SVM分类,否则使用KNN;在实际训练语料中,常常是负类样本数远多于正类样本数,而传统KNN方法对不平衡训练集存在敏感性,所以提出了用归一化的思想对传统的KNN方法进行修正.实验表明,用SVM与修正的KNN组合算法进行汉语专有名词抽取比单一的SVM方法以及原始的SVM-KNN方法更具优越性,而且这种方法可以推广到其他非平衡分布样本的分类问题.

  15. Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

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    Azizi E.

    2016-06-01

    Full Text Available Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method: In this algorithm, L-sec epochs of signals are displayed in form of a thirdorder tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN is used to classify the selected features. Results: The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion: Today, the lack of an automated system to detect or predict the seizure onset is strongly felt.

  16. Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

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    Rezaee, Kh.; Azizi, E.; Haddadnia, J.

    2016-01-01

    Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628

  17. A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing

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    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    Semi-supervised classifier design that simultaneously utilizes both a small number of labeled samples and a large number of unlabeled samples is a major research issue in machine learning. Existing semi-supervised learning methods for probabilistic classifiers belong to either generative or discriminative approaches. This paper focuses on a semi-supervised probabilistic classifier design for multiclass and single-labeled classification problems and first presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model, whose belongs to the same model family as the generative model, but whose parameters are different from the generative model. A hybrid classifier is constructed by combining both the generative and bias correction models based on the maximum entropy principle, where the combination weights of these models are determined so that the class labels of labeled samples are as correctly predicted as possible. We apply the hybrid approach to text classification problems by employing naive Bayes as the generative and bias correction models. In our experimental results on three English and one Japanese text data sets, we confirmed that the hybrid classifier significantly outperformed conventional probabilistic generative and discriminative classifiers when the classification performance of the generative classifier was comparable to the discriminative classifier.

  18. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

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    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  19. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

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    Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

    2008-09-01

    The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement

  20. 监督式谱空间分类器%Supervised Spectral Space Classifier

    Institute of Scientific and Technical Information of China (English)

    何萍; 徐晓华; 陈崚

    2012-01-01

    This paper proposes a nonlinear classification algorithm S3C (supervised spectral space classifier), short for supervised spectral space classifier. S3C integrates the discriminative information into the construction of the low-dimensional supervised spectral space. The input training data is mapped into the supervised spectral space, followed by the optimization of the partitioning hyperplane with maximum margin. The test data is also transformed into the same feature space via an intermediate "bridge" between the original feature space and the target feature space. The classification result of S3C is obtained by applying the optimal partitioning hyperplane to the transformed test data, directly. S3C enables researchers to examine the transformed data in the supervised spectral space, which is beneficial to both algorithm evaluation and parameter selection. Moreover, the study presents a supervised spectral space transformation algorithm (S3T) on the basis of S3C. S3T (supervised spectral space transformation) estimates the class indicating matrix by projecting the data from the supervised spectral space to the class indicating space. S3T can directly deal with multi-class classification problems, and it is more robust on the data sets containing noise. Experimental results on both synthetic and real-world data sets demonstrate the superiority of S3C and S3T algorithms compared with other state-of-the-art classification algorithms.%提出了一种非线性的监督式谱空间分类器(supervised spectral space classifier,简称S3C).S3C首先将输入数据映射到融合了训练数据判别信息的低维监督式谱空间中,然后在该监督式谱空间中构造最大化间隔的最优分割超平面,并把测试数据以无监督的方式也映射到与训练数据相同的新特征空间中,最后,直接应用之前构建的分类超平面对映射后的测试数据进行分类.由于S3C使研究者可以直观地观察到变化后的特征空间和映射

  1. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class....... A suitable pixel seed is manually implanted through visual inspection of the image classes. The best value for the window and the threshold are obtained from a spectral distance and heuristic criteria. This distance is calculated from a mathematical model of spectral separability. A pixel is incorporated...... into a region if a spectral homogeneity criterion is satisfied in the pixel-centered window for a given threshold. The homogeneity criterion is obtained from the model of spectral distance. The set of pixels forming a region represents a statistically valid sample of a defined class signaled by the initial...

  2. A supervised contextual classifier based on a region-growth algorithm

    DEFF Research Database (Denmark)

    Lira, Jorge; Maletti, Gabriela Mariel

    2002-01-01

    A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class...... pixel seed. The grown regions therefore constitute suitable training sets for each class. Comparing the statistical behavior of the pixel population of a sliding window with that of each class performs the classification. For region-growth, a window size is employed for each class. For classification....... A suitable pixel seed is manually implanted through visual inspection of the image classes. The best value for the window and the threshold are obtained from a spectral distance and heuristic criteria. This distance is calculated from a mathematical model of spectral separability. A pixel is incorporated...

  3. An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

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    Muller, Sybrand Jacobus; van Niekerk, Adriaan

    2016-07-01

    Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

  4. ELM-KNN for photometric redshift estimation of quasars

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    Zhang, Yanxia; Tu, Yang; Zhao, Yongheng; Tian, Haijun

    2017-06-01

    We explore photometric redshift estimation of quasars with the SDSS DR12 quasar sample. Firstly the quasar sample is separated into three parts according to different redshift ranges. Then three classifiers based on Extreme Learning Machine (ELM) are created in the three redshift ranges. Finally k-Nearest Neighbor (kNN) approach is applied on the three samples to predict photometric redshifts of quasars with multiwavelength photometric data. We compare the performance with different input patterns by ELM-KNN with that only by kNN. The experimental results show that ELM-KNN is feasible and superior to kNN (e.g. rms is 0.0751 vs. 0.2626 for SDSS sample), in other words, the ensemble method has the potential to increase regressor performance beyond the level reached by an individual regressor alone and will be a good choice when facing much more complex data.

  5. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

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    Luis D Lledó

    Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  6. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

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    Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás

    2015-01-01

    This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  7. Supervised Feature Subset Selection based on Modified Fuzzy Relative Information Measure for classifier Cart

    Directory of Open Access Journals (Sweden)

    K.SAROJINI,

    2010-06-01

    Full Text Available Feature subset selection is an essential task in data mining. This paper presents a new method for dealing with supervised feature subset selection based on Modified Fuzzy Relative Information Measure (MFRIM. First, Discretization algorithm is applied to discretize numeric features to construct the membership functions of each fuzzy sets of a feature. Then the proposed MFRIM is applied to select the feature subset focusing on boundary samples. The proposed method can select feature subset with minimum number of features, which are relevant to get higher average classification accuracy for datasets. The experimental results with UCI datasets show that the proposed algorithm is effective and efficient in selecting subset with minimum number of features getting higher average classification accuracy than the consistency based feature subset selection method.

  8. Classification in medical images using adaptive metric k-NN

    Science.gov (United States)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  9. Sensitivity study of a semiautomatic supervised classifier applied to minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Flesche, Harald

    1999-01-01

    spectroscopy (EDS) in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods are applied to perform the classification. Training sets are grown from one or a few seed points by a method that ensures spatial and spectral closeness of observations. Spectral closeness...... to a small area in order to allow for the estimation of a variance-covariance matrix. This expansion is controlled by upper limits for the spatial and Euclidean spectral distances from the seed point. Second, after this initial expansion the growing of the training set is controlled by an upper limit...... training, a standard quadratic classifier is applied. The performance for each parameter setting is measured by the overall misclassification rate on an independently generated validation set. The classification method is presently used as a routine petrographical analysis method at Norsk Hydro Research...

  10. Sensitivity study of a semiautomatic supervised classifier applied to minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Flesche, Harald

    2000-01-01

    spectroscopy (EDS) in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods are applied to perform the classification. Training sets are grown from one or a few seed points by a method that ensures spatial and spectral closeness of observations. Spectral closeness...... to a small area in order to allow for the estimation of a variance-covariance matrix. This expansion is controlled by upper limits for the spatial and Euclidean spectral distances from the seed point. Second, after this initial expansion the growing of the training set is controlled by an upper limit...... training, a standard quadratic classifier is applied. The performance for each parameter setting is measured by the overall misclassification rate on an independently generated validation set. The classification method is presently used as a routine petrographical analysis method at Norsk Hydro Research...

  11. Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

    Directory of Open Access Journals (Sweden)

    Tara Blakey

    2015-04-01

    Full Text Available We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images.

  12. A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition

    OpenAIRE

    Pao, Tsang-Long; Liao, Wen-Yuan; Chen, Yu-Te

    2008-01-01

    In this chapter, we present a speech emotion recognition system to compare several classifiers on the clean speech and noisy speech. Our proposed WD-KNN classifier outperforms the other three KNN-based classifiers at every SNR level and achieves highest accuracy from clean speech to 20dB noisy speech when compared with all other classifiers. Similar to (Neiberg et al, 2006), GMM is a feasible technique for emotion classification on the frame level and the results of GMM are better than perfor...

  13. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    Science.gov (United States)

    Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

  14. Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

    Science.gov (United States)

    Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

    1999-01-01

    In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

  15. Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Jian; Hamidouche, Khaled; Zheng, Jie; Lu, Xiaoyi; Vishnu, Abhinav; Panda, Dhabaleswar

    2015-08-05

    Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemic evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.

  16. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

    Science.gov (United States)

    Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

    2017-03-15

    Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (Pdoctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback

  17. 局部学习半监督多类分类机%Local learning semi-supervised multi-class classifier

    Institute of Scientific and Technical Information of China (English)

    吕佳; 邓乃扬; 田英杰; 邵元海; 杨新民

    2013-01-01

    半监督多类分类问题是机器学习和模式识别领域中的一个研究热点,目前大多数多类分类算法是将问题分解成若干个二类分类问题来求解.提出两种类标号表示方法来避免多个二类分类问题的求解,一种是单位圆类标号表示方法,一种是二进制序列类标号表示方法,并利用局部学习在二类分类问题中的良好学习特性,提出基于局部学习的半监督多类分类机.实验结果证明采用了基于局部学习的半监督多类分类机错分率更小,稳定性更高.%Semi-supervised multi-class classification problem opens research focuses in machine learning and pattern recognition, currently it is decomposed into a set of binary classification problems. Two kinds of class label presentation methods that one was class label presentation method of unit disc and the other was that of binary string were proposed for fear that multiple binary classification problems were solved. Besides, local learning has the good feature in semi-supervised binary classification problem. On the basis of it, local learning semi-supervised multi-class classifier was presented in this paper. The effectiveness of the algorithms was confirmed with experiments on benchmark datasets compared to other related algorithms.

  18. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

    2016-07-01

    The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

  19. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M.; Wallace, David J.; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R.; Hravnak, Marilyn

    2015-01-01

    OBJECTIVE Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral oximetry [SpO2]) recorded at 1/20Hz, and noninvasive oscillometric blood pressure [BP] less frequently, we partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were VS deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained ML algorithms. The best model was evaluated on alerts in the test set to enact online alert classification as signals evolve over time. MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve (AUC) performance of 0.79 (95% CI 0.67-0.93) for SpO2 at the instant the VS first crossed threshold and increased to 0.87 (95% CI 0.71-0.95) at 3 minutes into the alerting period. BP AUC started at 0.77 (95%CI 0.64-0.95) and increased to 0.87 (95% CI 0.71-0.98), while RR AUC started at 0.85 (95%CI 0.77-0.95) and increased to 0.97 (95% CI 0.94–1.00). HR alerts were too few for model development. CONCLUSIONS ML models can discern clinically relevant SpO2, BP and RR alerts from artifacts in an online monitoring dataset (AUC>0.87). PMID:26992068

  20. Cross-Layer Approach using k-NN Based Adaptive Modulation Coding (AMC and Incremental Redundancy Hybrid Automatic Repeat Request (IR-HARQ for MIMO

    Directory of Open Access Journals (Sweden)

    J. Sofia Priya Dharshini

    2014-09-01

    Full Text Available In MIMO Technology, a cross layer design enhances the spectral efficiency, reliability and throughput of the network. In this paper, a cross-layer approach using k-NN based Adaptive Modulation Coding (AMC and Incremental Redundancy Hybrid Automatic Repeat Request (IR-HARQ is proposed for MIMO Systems. The proposed cross layer approach connects physical layer and data link layer to enhance the performance of MIMO network. By means of MIMO fading channels, the coded symbols are forwarded in the physical layer on a frame by frame fashion subsequently using Space Time Block Coding (STBC. The receiver computes the signal to noise ratio (SNR and forwards back to the AMC controller. The controller selects a suitable MCS for the next transmission through k-NN classifier supervised learning algorithm. IR-HARQ is utilized at the data link layer to regulate packet retransmissions. The obtained results prove that the proposed technique has better performance in terms of throughput, BER and spectral efficiency

  1. QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.

    Science.gov (United States)

    Saini, Indu; Singh, Dilbag; Khosla, Arun

    2013-07-01

    The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.

  2. Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Michele Larobina

    2015-01-01

    Full Text Available The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN and principal component discriminant analysis (PCDA, and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction.

  3. Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images

    Science.gov (United States)

    Larobina, Michele; Murino, Loredana; Cervo, Amedeo; Alfano, Bruno

    2015-01-01

    The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction. PMID:26583131

  4. A multiple-point spatially weighted k-NN method for object-based classification

    Science.gov (United States)

    Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

    2016-10-01

    Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

  5. Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

    Directory of Open Access Journals (Sweden)

    Lei La

    2012-01-01

    Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

  6. An improved k-NN method based on multiple-point statistics for classification of high-spatial resolution imagery

    Science.gov (United States)

    Tang, Y.; Jing, L.; Li, H.; Liu, Q.; Ding, H.

    2016-04-01

    In this paper, the potential of multiple-point statistics (MPS) for object-based classification is explored using a modified k-nearest neighbour (k-NN) classification method (MPk-NN). The method first utilises a training image derived from a classified map to characterise the spatial correlation between multiple points of land cover classes, overcoming the limitations of two-point geostatistical methods, and then the spatial information in the form of multiple-point probability is incorporated into the k-NN classifier. The remotely sensed image of an IKONOS subscene of the Beijing urban area was selected to evaluate the method. The image was object-based classified using the MPk-NN method and several alternatives, including the traditional k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the MPk-NN approach can achieve greater classification accuracy relative to the alternatives, which are 82.05% and 89.12% based on pixel and object testing data, respectively. Thus, the proposed method is appropriate for object-based classification.

  7. SEMI-SUPERVISED RADIO TRANSMITTER CLASSIFICATION BASED ON ELASTIC SPARSITY REGULARIZED SVM

    Institute of Scientific and Technical Information of China (English)

    Hu Guyu; Gong Yong; Chen Yande; Pan Zhisong; Deng Zhantao

    2012-01-01

    Non-collaborative radio transmitter recognition is a significant but challenging issue,sinceit is hard or costly to obtain labeled training data samples.In order to make effective use of the unlabeled samples which can be obtained much easier,a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification.ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term.Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM,which use only labeled samples for classification,and also outperforms semi-supervised classifier LapSVM based on manifold regularization.

  8. A novel ensemble and composite approach for classifying proteins ...

    African Journals Online (AJOL)

    African Journal of Biotechnology ... For the fact that the location of proteins gave some details about the function of a protein whose location was ... (K-NN) classifiers, each of which was defined in a different pseudo amino composition vector.

  9. 结合SVM和KNN的Web日志挖掘技术研究方法%Research method of Web log mining technology with combination of SVM and KNN

    Institute of Scientific and Technical Information of China (English)

    曾俊

    2012-01-01

    将SVM和KNN算法结合在一起,组成一种新的Web文本分类算法-SVM-KNN算法.当Web文本和SVM最优超平面的距离大于预选设定的阈值,则采用SVM进行分类,反之采用SVM作为代表点的KNN算法对样本分类.实证结果表明,SVM-KNN分类算法的分类精度比单纯SVM或KNN分类算法有不同程度的提高,为Web数据挖掘提供了一种有效的分类方法.%This paper used SVM and KNN algorithm together to form a new classification algorithm for Web text-SVM-KNN algorithm. When optimal super plane distance of Web text and SVM was greater than the preselected threshold, used SVM to classify, otherwise it adopted KNN algorithm to classify the samples of SVM as the representative point. The experimental results show that the accuracy of SVM-KNN classification algorithm are better than pure SVM or KNN classification algorithm, and the Web text classification provides an effective classification method.

  10. Data characteristics that determine classifier performance

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2006-11-01

    Full Text Available classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...NN) classifier [8], the multi- layer perceptron (MLP) and support vector machines (SVMs) [9]. The NB, DT, kNN, MLP and SVM classifiers are all implementations of the machine learning package Weka [10]. The Gaussian classifier is a Matlab implementation...

  11. Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

    Science.gov (United States)

    Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

    2013-03-01

    Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

  12. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  13. Spectral-Spatial Hyperspectral Image Classification Based on KNN

    Science.gov (United States)

    Huang, Kunshan; Li, Shutao; Kang, Xudong; Fang, Leyuan

    2016-12-01

    Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral-spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.

  14. Hybrid k -Nearest Neighbor Classifier.

    Science.gov (United States)

    Yu, Zhiwen; Chen, Hantao; Liuxs, Jiming; You, Jane; Leung, Hareton; Han, Guoqiang

    2016-06-01

    Conventional k -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-the-art classification approaches.

  15. A Novel Approach for the Diagnosis of Diabetes and Liver Cancer using ANFIS and Improved KNN

    Directory of Open Access Journals (Sweden)

    C. Kalaiselvi

    2014-07-01

    Full Text Available The multi-factorial, chronicle, severe diseases are cancer and diabetes. As a result of abnormal level of glucose in body leads to heart attack, kidney disease, renal failure and cancer. Many studies have been proved that several types of cancer are possible in diabetes patients having a high blood sugar. Many approaches are proposed in the past to diagnose both cancer and diabetes. Even though the existing approaches are efficient one, the classification accuracy is poor. An Enhanced approach is proposed to achieve a higher efficiency and lower complexity. Adaptive neuro fuzzy inference system is used to classify the dataset with the help of adaptive group based KNN. The Pima Indian diabetes dataset are used as input dataset and classified based on the attribute information. The experimental result shows the classification accuracy is better than the existing approaches such FLANN, ANN with FUZZYKNN.

  16. A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2014-06-27

    Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

  17. Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric

    Directory of Open Access Journals (Sweden)

    Fen Wei

    2016-01-01

    Full Text Available In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP and weighted k-nearest neighbor classifier (WKNNC with an assembled matrix distance metric (AMDM is presented. Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor. Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM is presented to realize dimensional reduction of original high-dimensional feature tensor. Compared with classical second-order tensor locality preserving projection (STLPP, the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix. Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery. A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.

  18. Evaluation of normalization methods for cDNA microarray data by k-NN classification

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J

    2004-12-17

    Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double

  19. Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

    Science.gov (United States)

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-01-01

    Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

  20. Effects of poling process on KNN-modified piezoceramic properties

    OpenAIRE

    Marcos Rubio, F.; Romero, J. J.; Ochoa Guerrero, Diego A.; García García, José Eduardo; Pérez Pérez, Rafael; Fernández, José Francisco

    2010-01-01

    The influence of the orthorhombic to tetragonal phase transition near room temperature in the poling process of KNN-modified piezoceramics was studied. Poling temperatures of 25° and 120°C were used. The percentage of 90° domains reorientation induced by poling was evaluated trough X-ray diffraction analysis. The improvement of the piezoelectric properties when the poling temperature was 25°C could not be explained by the reorientation of 90° domains alone. Raman spectroscopy evidenced that t...

  1. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  2. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

    Directory of Open Access Journals (Sweden)

    Radoi Emanuel

    2006-01-01

    Full Text Available The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART, which is compared to two standard classifiers, MLP (multilayer perceptron and fuzzy KNN ( nearest neighbors. While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

  3. Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

    Science.gov (United States)

    Radoi, Emanuel; Quinquis, André; Totir, Felix

    2006-12-01

    The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN ([InlineEquation not available: see fulltext.] nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

  4. Efficient and Flexible KNN Query Processing in Real-Life Road Networks

    DEFF Research Database (Denmark)

    Lu, Yang; Bui, Bin; Zhao, Jiakui;

    2008-01-01

    Along with the developments of mobile services, effectively modeling road networks and efficiently indexing and querying network constrained objects has become a challenging problem. In this paper, we first introduce a road network model which captures real-life road networks better than previous...... models. Then, based on the proposed model, we propose a novel index named the RNG (Road Network Grid) index for accelerating KNN queries and continuous KNN queries over road network constrained data points. In contrast to conventional methods, speed limitations and blocking information of roads...... are included into the RNG index, which enables the index to support both distance-based and time-based KNN queries and continuous KNN queries. Our work extends previous ones by taking into account more practical scenarios, such as complexities in real-life road networks and time-based KNN queries. Extensive...

  5. Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification

    Science.gov (United States)

    Huang, Jian; Liu, Gui-xiong

    2016-09-01

    The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm ( k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z ' if T z ' was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.

  6. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  7. Synthesis and characterizations of BNT-BT-KNN ceramics for energy storage applications

    Science.gov (United States)

    Chandrasekhar, M.; Kumar, P.

    2016-09-01

    Dielectric, ferroelectric and piezoelectric properties of the (0.94-x) Bi0.5Na0.5TiO3-0.06BaTiO3-xK0.5Na0.5NbO3/BNT-BT-KNN ceramics with x = 0.02 and 0.05 (2KNN and 5KNN) were studied in detail. Dielectric study and temperature-dependent polarization hysteresis loops indicated a ferroelectric-to-antiferroelectric transition at depolarization temperature (Td). The low Td in both the ceramic samples suggested the dominant antiferroelectric ordering at room temperature (RT), which was also confirmed by RT polarization and strain hysteresis loops studies. Antiferroelectric-to-paraelectric phase transition temperature (Tm) was nearly same for both systems. The 5KNN ceramic samples showed the relaxor behaviour. The values of the dielectric constant, Td, and maximum strain percentage increased, whereas the coercive field and remnant polarization decreased with the increase of the KNN percentage in the BNT-BT-KNN system. High-energy storage density ∼0.5 J/cm3 at RT hinted about the suitability of the 5KNN system for energy storage applications.

  8. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... and integration possible, the field of molecular biology seems to be overwhelmingly homogeneous, and in need of heterogeneity and conflict to add drive and momentum to the work being carried out. The paper is based on observations of daily life in a molecular microbiology laboratory at the Technical University...

  9. Classifying Motion.

    Science.gov (United States)

    Duzen, Carl; And Others

    1992-01-01

    Presents a series of activities that utilizes a leveling device to classify constant and accelerated motion. Applies this classification system to uniform circular motion and motion produced by gravitational force. (MDH)

  10. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

    Directory of Open Access Journals (Sweden)

    D.A. Adeniyi

    2016-01-01

    Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

  11. Optimized Radial Basis Function Classifier for Multi Modal Biometrics

    Directory of Open Access Journals (Sweden)

    Anand Viswanathan

    2014-07-01

    Full Text Available Biometric systems can be used for the identification or verification of humans based on their physiological or behavioral features. In these systems the biometric characteristics such as fingerprints, palm-print, iris or speech can be recorded and are compared with the samples for the identification or verification. Multimodal biometrics is more accurate and solves spoof attacks than the single modal bio metrics systems. In this study, a multimodal biometric system using fingerprint images and finger-vein patterns is proposed and also an optimized Radial Basis Function (RBF kernel classifier is proposed to identify the authorized users. The extracted features from these modalities are selected by PCA and kernel PCA and combined to classify by RBF classifier. The parameters of RBF classifier is optimized by using BAT algorithm with local search. The performance of the proposed classifier is compared with the KNN classifier, Naïve Bayesian classifier and non-optimized RBF classifier.

  12. A monitoring and advisory system for diabetes patient management using a rule-based method and KNN.

    Science.gov (United States)

    Lee, Malrey; Gatton, Thomas M; Lee, Keun-Kwang

    2010-01-01

    Diabetes is difficult to control and it is important to manage the diabetic's blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN) classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA) programming.

  13. A Monitoring and Advisory System for Diabetes Patient Management Using a Rule-Based Method and KNN

    Directory of Open Access Journals (Sweden)

    Malrey Lee

    2010-04-01

    Full Text Available Diabetes is difficult to control and it is important to manage the diabetic’s blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA programming.

  14. The measurement of KNN and KLL in at 800 MeV

    Science.gov (United States)

    Glass, G.; Bhatia, T. S.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.; Verwest, B. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Riley, P. J.; Pepin, G. P.; Bonner, B. E.; Simmons, J. E.

    1983-09-01

    The spin-transfer parameters KNN and KLL have been measured for ? at 0° and 800 MeV for neutron momenta between 700 and {1200 MeV}/{c}. Peak values of KNN and KLL are -0.30 ± 0.05 and -0.5 ± 0.1 respectively. These results are in substantial disagreement with VerWest's field theoretic model.

  15. Fabrication of transparent lead-free KNN glass ceramics by incorporation method.

    Science.gov (United States)

    Yongsiri, Ploypailin; Eitssayeam, Sukum; Rujijanagul, Gobwut; Sirisoonthorn, Somnuk; Tunkasiri, Tawee; Pengpat, Kamonpan

    2012-02-16

    The incorporation method was employed to produce potassium sodium niobate [KNN] (K0.5Na0.5NbO3) glass ceramics from the KNN-SiO2 system. This incorporation method combines a simple mixed-oxide technique for producing KNN powder and a conventional melt-quenching technique to form the resulting glass. KNN was calcined at 800°C and subsequently mixed with SiO2 in the KNN:SiO2 ratio of 75:25 (mol%). The successfully produced optically transparent glass was then subjected to a heat treatment schedule at temperatures ranging from 525°C -575°C for crystallization. All glass ceramics of more than 40% transmittance crystallized into KNN nanocrystals that were rectangular in shape and dispersed well throughout the glass matrix. The crystal size and crystallinity were found to increase with increasing heat treatment temperature, which in turn plays an important role in controlling the properties of the glass ceramics, including physical, optical, and dielectric properties. The transparency of the glass samples decreased with increasing crystal size. The maximum room temperature dielectric constant (εr) was as high as 474 at 10 kHz with an acceptable low loss (tanδ) around 0.02 at 10 kHz.

  16. Natural language text classification and filtering with trigrams and evolutionary nearest neighbour classifiers

    NARCIS (Netherlands)

    Langdon, W.B.

    2000-01-01

    N~grams offer fast language independent multi-class text categorization. Text is reduced in a single pass to ngram vectors. These are assigned to one of several classes by a) nearest neighbour (KNN) and b) genetic algorithm operating on weights in a nearest neighbour classifier. 91 accuracy is found

  17. Frog sound identification using extended k-nearest neighbor classifier

    Science.gov (United States)

    Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

    2017-09-01

    Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

  18. An Improved KNN Algorithm Based on Multi-attribute Classification%基于多属性分类的KNN改进算法

    Institute of Scientific and Technical Information of China (English)

    张炯辉; 许尧舜

    2013-01-01

    To improve the classification accuracy of the conventional Euclidean KNN algorithm and the im-proved KNN algorithm based on information entropy,this paper proposes an improved KNN algorithm based on multi-attribute classification. The procedures of the new algorithm comprise:i) classify the attributes according to the percentage of their attribute values in an entire attribute of sample set into those discrete attributes suit-able for entropy-based KNN algorithm and those continuous attributes suitable for conventional Euclidean KNN similarity-based algorithm;ii) process the two types of attributes separately and then sum up the two series of results with weighing and put the sum as the distance between samples;iii) select k samples those are closest to the test sample to determine the decision attribute type of the test sample.%提出了一种基于多属性分类的KNN改进算法,可有效提高传统的欧几里德KNN算法和基于信息熵的KNN改进算法的分类准确度。首先,按照单个属性不同属性值的个数占整个属性包含样本的比例进行属性的分类,分为基于信息熵的KNN算法处理的离散属性和基于传统欧几里德KNN相似度处理的连续属性两类,然后分别对不同属性进行区别处理;其次,将两类不同处理后得到的结果按比例求和作为样本之间的距离;最后,选取与待测样本的距离最小的k个样本判断测试样本的决策属性类别。

  19. Prevention of Spammers and Promoters in Video Social Networks using SVM-KNN

    Directory of Open Access Journals (Sweden)

    Indira K

    2014-10-01

    Full Text Available As online social networks acquire larger user bases, they also become more interesting targets for spammers and promoters. Spam can take very different forms on social websites, especially in the form of videos and cannot always be detected by analyzing textual content. There are online video sharing systems that allow the users to post videos s response to any type of discussion topic. This feature encourages some of the users to post polluted content illegally as responses and there may be content promoters who try to promote them in the top listed search. Content pollution like spread advertise to generate sales, disseminate pornography, and compromise system reputation may threaten the trust of users on the system, thus weaken its success in promoting social interactions. As a solution for this problem, we classify the users as spammers, content promoters and legitimate users by building a test collection of real YouTube users using which we can provide a classification we use of content, individual and social attributes that help in characterizing each user class. For effective classification we use SVMKNN which is an active learning approach. Our proposed approach poses a promising alternative to simply considering all users as legitimate or to randomly selecting users for manual inspection. In simple SVM training is very slow on whole dataset and not works very well on multiple classes. To overcome this problem and to provide efficient classification in fast manner we proposed new approach is SVM-KNN. Train a Support Vector Machine on K no of collections of nearest neighbours.

  20. GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

    Directory of Open Access Journals (Sweden)

    Ahmed Shamsul Arefin

    Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

  1. 一种新的中文文本分类算法——One Class SVM-KNN算法%A New Text Classification Algorithm-One Class SVM-KNN

    Institute of Scientific and Technical Information of China (English)

    刘文; 吴陈

    2012-01-01

    中文文本分类在数据库及搜索引擎中得到广泛的应用,K-近邻(KNN)算法是常用于中文文本分类中的分类方法,但K-近邻在分类过程中需要存储所有的训练样本,并且直到待测样本需要分类时才建立分类,而且还存在类倾斜现象以及存储和计算的开销大等缺陷.单类SVM对只有一类的分类问题具有很好的效果,但不适用于多类分类问题,因此针对KNN存在的缺陷及单类SVM的特点提出One Class SVM-KNN算法,并给出了算法的定义及详细分析.通过实验证明此方法很好地克服了KNN算法的缺陷,并且查全率、查准率明显优于K-近邻算法.%Text classification is widely used in database and search engine. KNN is widely used in Chinese text categorization,however, KNN has many defects in the application of text classification. The deficiency of KNN classification algorithm is that all the training samples are kept until the samples are classified. When the size of samples is very large, the storage and computation will be costly, which will result in classification deviation. One class SVM is a simple and effective classification algorithm in one class. To solve KNN problems, a new algorithm based on harmonic one-class-SVM and KNN was proposed, which will achieve better classification effect. The experiment result is shown that the recall computed using the proposed method is obviously more highly than the KNN method.

  2. Efficient kNN Classification With Different Numbers of Nearest Neighbors.

    Science.gov (United States)

    Zhang, Shichao; Li, Xuelong; Zong, Ming; Zhu, Xiaofeng; Wang, Ruili

    2017-04-12

    k nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed k value (even though set by experts) to all test samples. Previous solutions assign different k values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal k values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal k values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal k values. In the test stage, the kTree fast outputs the optimal k value for each test sample, and then, the kNN classification can be conducted using the learned optimal k value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed k value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different k values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of

  3. 监督学习的发展动态%Current Directions in Supervised Learning Research

    Institute of Scientific and Technical Information of China (English)

    蒋艳凰; 周海芳; 杨学军

    2003-01-01

    Supervised learning is very important in machine learning area. It has been making great progress in manydirections. This article summarizes three of these directions ,which are the hot problems in supervised learning field.These three directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods forscaling up supervised learning algorithm, (c) extracting understandable rules from classifiers.

  4. Hybrid collaborative filtering algorithm based on KNN-SVM%基于KNN-SVM的混合协同过滤推荐算法

    Institute of Scientific and Technical Information of China (English)

    吕成戍; 王维国; 丁永健

    2012-01-01

    数据稀疏性问题对协同过滤推荐系统的推荐精度有很大影响,为此,融合缺失数据平衡方法,提出了一个基于KNN-SVM的混合协同过滤推荐算法.利用K-最近邻法对训练集中的缺失数据进行填补,然后通过支持向量机交叉验证进行分类,综合两者优点,从而克服数据质量对推荐算法的影响.在标杆数据集上进行了仿真实验,数值结果证明了方法的有效性.%The problem of data sparsenees has great influence on collaborative filtering recommendation system' s accuracy, balance for this missing data fusion method, this paper proposed a hybrid collaborative filtering algorithms based on KNN-SVM. K-nearest neighbor method used the training set to fill the missing data, and then cross-validated by SVM classification. Comprehend advantages both KNN and SVM in order to overcome impact of data quality on the recommended algorithm. The proposed approach was applied to benchmark problems, and the simulation results show it is valid.

  5. The measurement of KNN and KLL in p↘p → n↘X at 800 MeV

    Science.gov (United States)

    Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Riley, P. J.; Pepin, G. P.; Bonner, B. E.; Simmons, J. E.

    1981-03-01

    The spin transfer parameters, KNN and KLL have been measured in p↘p → n↘X at 0° and 800 MeV for neutron momenta between 700 and 1200 MeV/c. Peak values of KNN and KLL are -.3±.05 and -.5±.1 respectively.

  6. An Analysis of Document Category Prediction Responses to Classifier Model Parameter Treatment Permutations within the Software Design Patterns Subject Domain

    Science.gov (United States)

    Pankau, Brian L.

    2009-01-01

    This empirical study evaluates the document category prediction effectiveness of Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier treatments built from different feature selection and machine learning settings and trained and tested against textual corpora of 2300 Gang-Of-Four (GOF) design pattern documents. Analysis of the experiment's…

  7. Maximum-entropy parameter estimation for the k-NN modified value-difference kernel

    NARCIS (Netherlands)

    Hendrickx, I.H.E.; van den Bosch, A.; Verbruggen, R.; Taatgen, N.; Schomaker, L.

    2004-01-01

    We introduce an extension of the modified value-difference kernel of $k$-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybri

  8. Maximum-Entropy Parameter Estimation for the k-nn Modified Value-Difference Kernel

    NARCIS (Netherlands)

    Hendrickx, Iris; Bosch, Antal van den

    2005-01-01

    We introduce an extension of the modified value-difference kernel of k-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid

  9. Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

    Science.gov (United States)

    Mei, Gang; Xu, Nengxiong; Xu, Liangliang

    2016-01-01

    This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

  10. Photometric redshift estimation for quasars by integration of KNN and SVM

    Science.gov (United States)

    Han, Bo; Ding, Hong-Peng; Zhang, Yan-Xia; Zhao, Yong-Heng

    2016-05-01

    The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding z phot. Our analysis has found two dense regions with catastrophic failure, one in the range of z phot ɛ [0.3, 1.2] and the other in the range of zphot ɛ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).

  11. Identification of Data Fragment Classification Algorithm Based on PCA-LDA and KNN-SMO%基于 PCA-LDA 和 KNN-SMO 的数据碎片分类识别算法

    Institute of Scientific and Technical Information of China (English)

    傅德胜; 经正俊

    2015-01-01

    在计算机取证领域,数据碎片的取证分析已成为获取数字证据的一种重要手段。本文针对取证中数据碎片的取证问题提出了一种新的基于内容特征的数据碎片类型识别算法,该方法首先对数据碎片进行分块主成分分析PCA 后,对 PCA 特征向量进行线性鉴别分析 LDA 获取组合特征向量,然后利用 K 最邻近 KNN 算法和序列最小优化SMO 算法组成融合分类器,运用获取的组合特征向量对数据碎片进行分类识别。实验表明,该算法与其他相关算法相比,具有较高的识别准确率和识别速率,取得了良好的识别效果。%In the computer forensics field, the forensic analysis of data fragment has become an important means to obtain digital evidence. Aiming at the problem of data fragment forensics, this paper proposes a novel algorithm of data classification identification based on the content feature. Firstly, it makes principal component analysis (PCA) of each blocks in the data fragment; secondly, it makes linear discriminant analysis (LDA) of each PCA feature vector so as to get the combinational feature vector; finally, the author identifies the type of data fragment with the combinational fea-ture vector by using the fusion classifier of k nearest neighbor (KNN) algorithm and sequential minimal optimization algorithm (SMO). Experimental results have shown that compared with the related algorithms the proposed algorithm has better identification accuracy and identification rate which achieves better identification results.

  12. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  13. Dynamic & Attribute Weighted KNN for Document Classification Using Bootstrap Sampling

    Directory of Open Access Journals (Sweden)

    Dharmendra S Panwar,

    2014-11-01

    Full Text Available Although publicly accessible databases containing speech documents. It requires a great deal of time and effort required to keep them up to date is often burdensome. In an effort to help identify speaker of speech if text is available, text-mining tools, from the machine learning discipline, it can be applied to help in this process also. Here, we describe and evaluate document classification algorithms i.e. a combo pack of text mining and classification. This task asked participants to design classifiers for identifying documents containing speech related information in the main literature, and evaluated them against one another. Expected systems utilizes a novel approach of k -nearest neighbour classification and compare its performance by taking different values of k.

  14. 基于K-均值聚类的小样本集KNN分类算法%KNN CLASSIFICATION ALGORITHM FOR SMALL SAMPLE SETS BASED ON K-MEANS CLUSTERING

    Institute of Scientific and Technical Information of China (English)

    刘应东; 牛惠民

    2011-01-01

    When KNN and its improved algorithms are performing classification, it always influences the final classification accuracy because of either too dense or too few the samples or too large the density differences among various kinds of samples. The paper proposes a small sample set KNN classification algorithm based on clustering technology. A new sample set is generated through clustering and editing which contains various kinds of samples with close densities. That new sample set is used to classify and label data objects whose classification and label numbers are unknown. Tests by standard data sets reveal that the algorithm can improve KNN classification accuracy and obtain satisfactory results.%KNN及其改进算法进行分类时,如样本集中、样本过少或各类样本的密度差异较大,都将会影响最后的分类精度.提出一种基于聚类技术的小样本集KNN分类算法.通过聚类和剪理,形成各类的样本密度接近的新的样本集,并利用该新样本集对类标号未知数据对象进行类别标识.通过使用标准数据集的测试,发现该算法能够提高KNN的分类精度,取得了较满意的结果.

  15. Document Classification Using Expectation Maximization with Semi Supervised Learning

    CERN Document Server

    Nigam, Bhawna; Salve, Sonal; Vamney, Swati

    2011-01-01

    As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is "Dynamically Generation of New Class". The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.

  16. Supervision as Metaphor

    Science.gov (United States)

    Lee, Alison; Green, Bill

    2009-01-01

    This article takes up the question of the language within which discussion of research degree supervision is couched and framed, and the consequences of such framings for supervision as a field of pedagogical practice. It examines the proliferation and intensity of metaphor, allegory and allusion in the language of candidature and supervision,…

  17. A Supervision of Solidarity

    Science.gov (United States)

    Reynolds, Vikki

    2010-01-01

    This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

  18. A cascade classifier for diagnosis of melanoma in clinical images.

    Science.gov (United States)

    Sabouri, P; GholamHosseini, H; Larsson, T; Collins, J

    2014-01-01

    Computer aided diagnosis of medical images can help physicians in better detecting and early diagnosis of many symptoms and therefore reducing the mortality rate. Realization of an efficient mobile device for semi-automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this paper, interactive object recognition methodology is adopted for border segmentation of clinical skin lesion images. In addition, performance of five classifiers, KNN, Naïve Bayes, multi-layer perceptron, random forest and SVM are compared based on color and texture features for discriminating melanoma from benign nevus. The results show that a sensitivity of 82.6% and specificity of 83% can be achieved using a single SVM classifier. However, a better classification performance was achieved using a proposed cascade classifier with the sensitivity of 83.06% and specificity of 90.05% when performing ten-fold cross validation.

  19. Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

    OpenAIRE

    Kurth, Thorsten; Zhang, Jian; Satish, Nadathur; Mitliagkas, Ioannis; Racah, Evan; Patwary, Mostofa Ali; Malas, Tareq; Sundaram, Narayanan; Bhimji, Wahid; Smorkalov, Mikhail; Deslippe, Jack; Shiryaev, Mikhail; Sridharan, Srinivas; Prabhat; Dubey, Pradeep

    2017-01-01

    This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employin...

  20. Projected estimators for robust semi-supervised classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... proposed in this work does not rely on assumptions that are not intrinsic to the classifier at hand. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. More...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...

  1. Processing and characterizations of BNT-KNN ceramics for actuator applications

    Directory of Open Access Journals (Sweden)

    Mallam Chandrasekhar

    2016-06-01

    Full Text Available BNT-KNN powder (with composition 0.93Bi0.5Na0.5TiO3–0.07K0.5Na0.5NbO3 was synthesized as a single perovskite phase by conventional solid state reaction route and dense ceramics were obtained by sintering of powder compacts at 1100 °C for 4 h. Dielectric study confirmed relaxor behaviour, whereas the microstructure study showed sharp cornered cubic like grains with an average grain size ∼1.15 µm. The saturated polarization vs. electric field (P-E hysteresis loops confirmed the ferroelectric (FE nature while the butterfly shaped strain vs. electric field (S-E loops suggested the piezoelectric nature of the BNT-KNN ceramic samples. Maximum electric field induced strain of ∼0.62% suggested the usefulness of this system for actuator applications.

  2. Continually Answering Constraint k-NN Queries in Unstructured P2P Systems

    Institute of Scientific and Technical Information of China (English)

    Bin Wang; Xiao-Chun Yang; Guo-Ren Wang; Ge Yu; Lei Chen; X. Sean Wang,; Xue-Min Lin

    2008-01-01

    We consider the problem of efficiently computing distributed geographical k-NN queries in an unstructured peer-to-peer (P2P) system, in which each peer is managed by an individual organization and can only communicate with its logical neighboring peers. Such queries are based on local filter query statistics, and require as less communication cost as possible, which makes it more difficult than the existing distributed k-NN queries. Especially, we hope to reduce candidate peers and degrade communication cost. In this paper, we propose an efficient pruning technique to minimize the number of candidate peers to be processed to answer the k-Nnqueries. Our approach is especially suitable for continuous k-Nnqueries when updating peers, including changing ranges of peers, dynamically leaving or joining peers, and updating data in a peer.In addition, simulation results show that the proposed approach outperforms the existing Minimum Bounding Rectangle (MBR)-based query approaches, especially for continuous queries.

  3. The First-Principle Calculation of La-doping Effect on Piezoelectricity in Tetragonal KNN Crystal

    Science.gov (United States)

    Zhang, Qiaoli; Zhu, Jiliang; Yuan, Daqing; Zhu, Bo; Wang, Mingsong; Zhu, Xiaohong; Fan, Ping; Zuo, Yi; Zheng, Yongnan; Zhu, Shengyun

    2012-05-01

    The La-dopping effect on the piezoelectricity in the K0.5Na0.5NbO3 (KNN) crystal with a tetragonal phase is investigated for the first time using the first-principle calculation based on density functional theory. The full potentiallinearized augumented plane wave plus local orbitals (APW-LO) method and the supercell method are used in the calculation for the KNN crystal with and without the La doping. The results show that the piezoelectricity originates from the strong hybridization between the Nb atom and the O atom, and the substitution of the K or Na atom by the La impurity atom introduces the anisotropic relaxation and enhances the piezoelectricity at first and then restrains the hybridization of the Nb-O atoms when the La doping content further increases.

  4. KNN/BNT Composite Lead-Free Films for High-Frequency Ultrasonic Transducer Applications

    Science.gov (United States)

    Lau, Sien Ting; Ji, Hong Fen; Li, Xiang; Ren, Wei; Zhou, Qifa; Shung, K. Kirk

    2011-01-01

    Lead-free K0.5Na0.5NbO3/Bi0.5Na0.5TiO3 (KNN/BNT) films have been fabricated by a composite sol-gel technique. Crystalline KNN fine powder was dispersed in the BNT precursor solution to form a composite slurry which was then spin-coated onto a platinum-buffered Si substrate. Repeated layering and vacuum infiltration were applied to produce 5-μm-thick dense composite film. By optimizing the sintering temperature, the films exhibited good dielectric and ferroelectric properties comparable to PZT films. A 193-MHz high-frequency ultrasonic transducer fabricated from this composite film showed a −6-dB bandwidth of approximately 34%. A tungsten wire phantom was imaged to demonstrate the capability of the transducer. PMID:21244994

  5. A record flexible piezoelectric KNN ultrafine-grained nanopowder-based nanogenerator

    Directory of Open Access Journals (Sweden)

    Qing-tang Xue

    2015-01-01

    Full Text Available We explore a type piezoelectric material 0.9525(K0.5Na0.5NbO3-0.0475LiTaO3 (KNN-LTS which can be used to fabricate nanogenerator with high output voltage and current due to its high piezoelectric constant (d33. Because of its unique structure mixed with multi-wall carbon nanotube and polydimethylsiloxane, the output voltage is up to 53 V and the output current is up to 15 uA (current density of 12.5 uA/cm2 respectively. The value of the output voltage and output current represent the highest level in the piezoelectric field reported to date. The KNN-LTS nanopowder-based nanogenerator can also be used as a sensitive motion detection sensor.

  6. Supervised pixel classification for segmenting geographic atrophy in fundus autofluorescene images

    Science.gov (United States)

    Hu, Zhihong; Medioni, Gerard G.; Hernandez, Matthias; Sadda, SriniVas R.

    2014-03-01

    Age-related macular degeneration (AMD) is the leading cause of blindness in people over the age of 65. Geographic atrophy (GA) is a manifestation of the advanced or late-stage of the AMD, which may result in severe vision loss and blindness. Techniques to rapidly and precisely detect and quantify GA lesions would appear to be of important value in advancing the understanding of the pathogenesis of GA and the management of GA progression. The purpose of this study is to develop an automated supervised pixel classification approach for segmenting GA including uni-focal and multi-focal patches in fundus autofluorescene (FAF) images. The image features include region wise intensity (mean and variance) measures, gray level co-occurrence matrix measures (angular second moment, entropy, and inverse difference moment), and Gaussian filter banks. A k-nearest-neighbor (k-NN) pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. A voting binary iterative hole filling filter is then applied to fill in the small holes. Sixteen randomly chosen FAF images were obtained from sixteen subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by certified graders. Two-fold cross-validation is applied for the evaluation of the classification performance. The mean Dice similarity coefficients (DSC) between the algorithm- and manually-defined GA regions are 0.84 +/- 0.06 for one test and 0.83 +/- 0.07 for the other test and the area correlations between them are 0.99 (p < 0.05) and 0.94 (p < 0.05) respectively.

  7. Neural Gen Feature Selection for Supervised Learning Classifier

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-04-01

    Full Text Available Face recognition has recently received significant attention, especially during the past few years. Many face recognition techniques were developed such as PSO-SVM and LDA-SVM However, inefficient features in the face recognition may lead to inadequate in the recognition results. Hence, a new face recognition system based on Genetic Algorithm and FFBNN technique is proposed. Our proposed face recognition system initially performs the feature extraction and these optimal features are promoted to the recognition process. In the feature extraction, the optimal features are extracted from the face image database by Genetic Algorithm (GA with FFBNN and the computed optimal features are given to the FFBNN technique to carry out the training and testing process. The optimal features from the feature database are fed to the FFBNN for accomplishing the training process. The well trained FFBNN with the optimal features provide the recognition result. The optimal features in FFBNN by GA efficiently perform the face recognition process. The human face dataset called YALE is utilized to analyze the performance of our proposed GA-FFNN technique and also this GA-FFBNN is compared with standard SVM and PSO-SVM techniques.

  8. Weakly supervised learning of a classifier for unusual event detection.

    Science.gov (United States)

    Jäger, Mark; Knoll, Christian; Hamprecht, Fred A

    2008-09-01

    In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

  9. A multiple classifier system for early melanoma diagnosis.

    Science.gov (United States)

    Sboner, Andrea; Eccher, Claudio; Blanzieri, Enrico; Bauer, Paolo; Cristofolini, Mario; Zumiani, Giuseppe; Forti, Stefano

    2003-01-01

    Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using different classification algorithms. In this paper, we will illustrate a novel approach by which a suitable combination of different classifiers is used in order to improve the diagnostic performances of single classifiers. We used three different kinds of classifiers, namely linear discriminant analysis (LDA), k-nearest neighbour (k-NN) and a decision tree, the inputs of which are 38 geometric and colorimetric features automatically extracted from digital images of skin lesions. Multiple classifiers were generated by combining the diagnostic outputs of single classifiers with appropriate voting schemata. This approach was evaluated on a set of 152 digital skin images. We compared the performances of multiple classifiers (2- and 3-classifier groups) between them and with respect to single ones (1-classifier group). We further compared the classifiers' performances with those of eight dermatologists. Classifiers' performances were measured in terms of distance from the ideal classifier. Compared with 1- and 2-classifier groups, performances of 3-classifier systems were significantly higher (Pclassifier groups (P=0.352). While the dermatologists group showed a level of performances significantly higher than the 1-classifier systems (Pclassifier groups and the dermatologists groups, indicating comparable performances. This work suggests that a suitable combination of different kinds of classifiers can improve the performances of an automatic diagnostic system.

  10. PHYSICAL AND ELECTRICAL PROPERTIES ENHANCEMENT OF RARE-EARTH DOPED-POTASSIUM SODIUM NIOBATE (KNN: A REVIEW

    Directory of Open Access Journals (Sweden)

    Akmal Mat Harttat Maziati

    2015-06-01

    Full Text Available Alkaline niobate mainly potassium sodium niobate, (KxNa1-x NbO3 (abreviated as KNN has long attracted attention as piezoelectric materials as its high Curie temperature (Tc and piezoelectric properties. The volatility of alkaline element (K, Na is, however detrimental to the stoichiometry of KNN, contributing to the failure to achieve high-density structure and lead to the formation of intrinsic defects. By partially doping of several rare-earth elements, the inherent defects could be improved significantly. Therefore, considerable attempts have been made to develop doped-KNN based ceramic materials with high electrical properties. In this paper, these research activities are reviewed, including dopants type and doping role in KNN perovskite structure.

  11. A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

    Directory of Open Access Journals (Sweden)

    JORGE FLORES CRUZ

    2017-01-01

    Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

  12. Good supervision and PBL

    DEFF Research Database (Denmark)

    Otrel-Cass, Kathrin

    This field study was conducted at the Faculty of Social Sciences at Aalborg University with the intention to investigate how students reflect on their experiences with supervision in a PBL environment. The overall aim of this study was to inform about the continued work in strengthening supervision...... at this faculty. This particular study invited Master level students to discuss: • How a typical supervision process proceeds • How they experienced and what they expected of PBL in the supervision process • What makes a good supervision process...

  13. Improving Estimation Accuracy of Quasars’ Photometric Redshifts by Integration of KNN and SVM

    Science.gov (United States)

    Han, Bo; Ding, Hongpeng; Zhang, Yanxia; Zhao, Yongheng

    2015-08-01

    The massive photometric data collected from multiple large-scale sky surveys offers significant opportunities for measuring distances of many celestial objects by photometric redshifts zphot in a wide coverage of the sky. However, catastrophic failure, an unsolved problem for a long time, exists in the current photometric redshift estimation approaches (such as k-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding zphot. By analysis, we observe two dense regions with catastrophic failure, one in the range of zphot [0.1,1.1], the other in the range of zphot [1.5,2.5]. In the second stage, we map the photometric multiband input pattern of points falling into the two ranges from original attribute space into high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space, many bad estimation points resulted from catastrophic failure by using simple Euclidean distance computation in KNN can be identified by classification hyperplane SVM and further be applied correction. Experimental results based on SDSS data for quasars showed that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshift.

  14. Lead-free piezoelectric KNN-BZ-BNT films with a vertical morphotropic phase boundary

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2015-07-01

    Full Text Available The lead-free piezoelectric 0.915K0.5Na0.5NbO3-0.075BaZrO3-0.01Bi0.5Na0.5TiO3 (0.915KNN-0.075BZ-0.01BNT films were prepared by a chemical solution deposition method. The films possess a pure rhomobohedral perovskite phase and a dense surface without crack. The temperature-dependent dielectric properties of the specimens manifest that only phase transition from ferroelectric to paraelectric phase occurred and the Curie temperature is 217 oC. The temperature stability of ferroelectric phase was also supported by the stable piezoelectric properties of the films. These results suggest that the slope of the morphotropic phase boundary (MPB for the solid solution formed with the KNN and BZ in the films should be vertical. The voltage-induced polarization switching, and a distinct piezo-response suggested that the 0.915 KNN-0.075BZ-0.01BNT films show good piezoelectric properties.

  15. Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

    Directory of Open Access Journals (Sweden)

    A. Moosavian

    2013-01-01

    Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

  16. Combination of designed immune based classifiers for ERP assessment in a P300-based GKT

    Directory of Open Access Journals (Sweden)

    Mohammad Hassan Moradi

    2012-08-01

    Full Text Available Constructing a precise classifier is an important issue in pattern recognition task. Combination the decision of several competing classifiers to achieve improved classification accuracy has become interested in many research areas. In this study, Artificial Immune system (AIS as an effective artificial intelligence technique was used for designing of several efficient classifiers. Combination of multiple immune based classifiers was tested on ERP assessment in a P300-based GKT (Guilty Knowledge Test. Experiment results showed that the proposed classifier named Compact Artificial Immune System (CAIS was a successful classification method and could be competitive to other classifiers such as K-nearest neighbourhood (KNN, Linear Discriminant Analysis (LDA and Support Vector Machine (SVM. Also, in the experiments, it was observed that using the decision fusion techniques for multiple classifier combination lead to better recognition results. The best rate of recognition by CAIS was 80.90% that has been improved in compare to other applied classification methods in our study.

  17. SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION

    Directory of Open Access Journals (Sweden)

    Ashis Kumar Mandal

    2014-09-01

    Full Text Available This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5, K-Nearest Neighbour (KNN, Naïve Bays (NB, and Support Vector Machine (SVM for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.

  18. DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

    Science.gov (United States)

    Liang, Zhaohui; Huang, Jimmy Xiangji; Zeng, Xing; Zhang, Gang

    2016-08-10

    Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions. A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov

  19. The fuzzy gene filter: A classifier performance assesment

    CERN Document Server

    Perez, Meir

    2011-01-01

    The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.

  20. Classification in medical image analysis using adaptive metric k-NN

    DEFF Research Database (Denmark)

    Chen, Chen; Chernoff, Konstantin; Karemore, Gopal

    2010-01-01

    with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based...... on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure...

  1. GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

    Science.gov (United States)

    Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

    2016-08-01

    In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

  2. A comparison of the spatial linear model to Nearest Neighbor (k-NN) methods for forestry applications.

    Science.gov (United States)

    Ver Hoef, Jay M; Temesgen, Hailemariam

    2013-01-01

    Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages.

  3. Networks of Professional Supervision

    Science.gov (United States)

    Annan, Jean; Ryba, Ken

    2013-01-01

    An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

  4. Forskellighed i supervision

    DEFF Research Database (Denmark)

    Petersen, Birgitte; Beck, Emma

    2009-01-01

    Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008......Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008...

  5. Experiments in Virtual Supervision.

    Science.gov (United States)

    Walker, Rob

    This paper examines the use of First Class conferencing software to create a virtual culture among research students and as a vehicle for supervision and advising. Topics discussed include: computer-mediated communication and research; entry to cyberculture, i.e., research students' induction into the research community; supervision and the…

  6. Deterministic Pattern Classifier Based on Genetic Programming

    Institute of Scientific and Technical Information of China (English)

    LI Jian-wu; LI Min-qiang; KOU Ji-song

    2001-01-01

    This paper proposes a supervised training-test method with Genetic Programming (GP) for pattern classification. Compared and contrasted with traditional methods with regard to deterministic pattern classifiers, this method is true for both linear separable problems and linear non-separable problems. For specific training samples, it can formulate the expression of discriminate function well without any prior knowledge. At last, an experiment is conducted, and the result reveals that this system is effective and practical.

  7. Enhancing Patient Safety Event Reporting by K-nearest Neighbor Classifier.

    Science.gov (United States)

    Liang, Chen; Gong, Yang

    2015-01-01

    Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structured data entry or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The present study suggested a text classification method, k-nearest neighbor (KNN), for predicting subject categories as in our proposed reporting system. Our results demonstrated the feasibility of KNN classifier used for text classification and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.

  8. Role of sintering time, crystalline phases and symmetry in the piezoelectric properties of lead-free KNN-modified ceramics

    Energy Technology Data Exchange (ETDEWEB)

    Rubio-Marcos, F., E-mail: frmarcos@icv.csic.es [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain); Marchet, P.; Merle-Mejean, T. [SPCTS, UMR 6638 CNRS, Universite de Limoges, 123, Av. A. Thomas, 87060 Limoges (France); Fernandez, J.F. [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain)

    2010-09-01

    Lead-free KNN-modified piezoceramics of the system (Li,Na,K)(Nb,Ta,Sb)O{sub 3} were prepared by conventional solid-state sintering. The X-ray diffraction patterns revealed a perovskite phase, together with some minor secondary phase, which was assigned to K{sub 3}LiNb{sub 6}O{sub 17}, tetragonal tungsten-bronze (TTB). A structural evolution toward a pure tetragonal structure with the increasing sintering time was observed, associated with the decrease of TTB phase. A correlation between higher tetragonality and higher piezoelectric response was clearly evidenced. Contrary to the case of the LiTaO{sub 3} modified KNN, very large abnormal grains with TTB structure were not detected. As a consequence, the simultaneous modification by tantalum and antimony seems to induce during sintering a different behaviour from the one of LiTaO{sub 3} modified KNN.

  9. Photometric Redshift Estimation for Quasars by Integration of KNN and SVM

    CERN Document Server

    Han, Bo; Zhang, Yanxia; Zhao, Yongheng

    2016-01-01

    The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding z$_{\\rm phot}$. By analysis, we find two dense regions with catastrophic failure, one in the range of z$_{\\rm phot}\\in[0.3,1.2]$, the other in the range of z$_{\\rm phot}\\in [1.2,2.1]$. In the second stage, we map the photometric input pattern of points falling into the two ranges from original attribute space into a high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space,...

  10. BC-iDistance: an optimized high-dimensional index for KNN processing

    Institute of Scientific and Technical Information of China (English)

    LIANG Jun-jie; FENG Yu-cai

    2008-01-01

    To facilitate high-dimensional KNN queries, based on techniques of approximate vector presentation and one-dimensional transformation, an optimal index is proposed, namely Bit-Code based iDistance ( BC-iDis-tance). To overcome the defect of much information loss for iDistance in one-dimensional transformation, the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector, and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively. By employing the classical B + tree, this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing. Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.

  11. A New Method to Improve the Electrical Properties of KNN-based Ceramics: Tailoring Phase Fraction

    KAUST Repository

    Lv, Xiang

    2017-08-18

    Although both the phase type and fraction of multi-phase coexistence can affect the electrical properties of (K,Na)NbO3 (KNN)-based ceramics, effects of phase fraction on their electrical properties were few concerned. In this work, through changing the calcination temperature of CaZrO3 powders, we successfully developed the 0.96K0.5Na0.5Nb0.96Sb0.04O3-0.01CaZrO3-0.03Bi0.5Na0.5HfO3 ceramics containing a wide rhombohedral-tetragonal (R-T) phase coexistence with the variations of T (or R) phase fractions. It was found that higher T phase fraction can warrant a larger piezoelectric constant (d33) and d33 also showed a linear variation with respect to tetragonality ratio (c/a). More importantly, a number of domain patterns were observed due to high T phase fraction and large c/a ratio, greatly benefiting the piezoelectricity. In addition, the improved ferroelectric fatigue behavior and thermal stability were also shown in the ceramics containing high T phase fraction. Therefore, this work can bring a new viewpoint into the physical mechanism of KNN-based ceramics behind R-T phase coexistence.

  12. Improving Shape Retrieval by Integrating AIR and Modified Mutual kNN Graph

    Directory of Open Access Journals (Sweden)

    Nouman Qadeer

    2015-01-01

    Full Text Available In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutual kNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutual kNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.

  13. Supervision som undervisningsform i voksenspecialundervisningen

    DEFF Research Database (Denmark)

    Kristensen, René

    2000-01-01

    Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne.......Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne....

  14. Obtaining of BNKT-KNN ceramic powders by the Pechini Method; Obtencion de polvos ceramicos de BNKT-KNN por el metodo Pechini

    Energy Technology Data Exchange (ETDEWEB)

    Yasno, J. P.; Tirado-Mejia, L.; Kiminami, R.; Gaona, J.; Raigoza, C. F. V.

    2013-09-01

    Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi{sub 1}/2Na{sub 1}/2)1-x(Bi{sub 1}/2K{sub 1}/2)xTiO{sub 3}]-0,03[(Na{sub 1}/2K{sub 1}/2)NbO{sub 3}] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27). This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 degree centigrade. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.

  15. Obtaining of BNKT-KNN ceramic powders by the Pechini Method; Obtencion de polvos ceramicos de BNKT-KNN por el metodo Pechini

    Energy Technology Data Exchange (ETDEWEB)

    Yasno, J. P.; Tirado-Mejia, L.; Kiminamp, R. H. G. A.; Gaona, J. S.; Raigoza, C. E. V.

    2013-10-01

    Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi{sub 1}/2Na{sub 1}/2){sub 1}-x(Bi{sub 1}/2K{sub 1}/2)xTiO{sub 3}]-0,03[(Na{sub 1}/2K{sub 1}/2)NbO{sub 3}] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27). This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 degree centigrade. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy. (Author)

  16. Measurement of KNN, KSS, KSL, and KLL in n↘p → p↘n at 800 MeV in the CEX region

    Science.gov (United States)

    Ransome, R. D.; Hollas, C. L.; Riley, P. J.; Bonner, B. E.; Gibbs, W. R.; McNaughton, M. W.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.

    1981-03-01

    The spin transfer parameters1 KNN, KSS, and KLL have been measured for np elastic scattering at 800 MeV between 165° and 180° c.m. The parameters KNN and KLL are in good agreement with the quasi-free reaction p↘d → n↘pp at 180°.2

  17. Missing Data Imputation for Supervised Learning

    OpenAIRE

    Poulos, Jason; Valle, Rafael

    2016-01-01

    This paper compares methods for imputing missing categorical data for supervised learning tasks. The ability of researchers to accurately fit a model and yield unbiased estimates may be compromised by missing data, which are prevalent in survey-based social science research. We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on non-imputed (i.e., one-hot encoded) or imputed data with different degrees of missing-data perturbat...

  18. KNN Text Classification Algorithm Based on Chaotic Binary Particle Swarm Optimization%基于混沌二进制粒子群优化的KNN文本分类算法

    Institute of Scientific and Technical Information of China (English)

    徐辉

    2012-01-01

    中文文本分类的主要问题是特征空间的高维性.提出了基于混沌二进制粒子群的KNN文本分类算法,利用混沌二进制粒子群算法遍历训练集的特征空间,选择特征子空间,然后在特征子空间中使用KNN算法进行文本分类.在粒子群的迭代优化过程中,利用混沌映射,指导群体进行混沌搜索,使算法摆脱局部最优,扩大寻找全局最优解的能力.实验结果表明,提出的新分类算法对中文文本分类是有效的,其分类准确率、召回率都优于KNN算法.%The main problem of Chinese text classification is the high dimenmonat teature space particle swarm optimization, KNN text classification algorithm is proposed. It uses chaotic particle swarm algorithm to traverse feature space of the training set, selects the feature subspace, and then it uses KNN algorithm to classify text in feature subspace. In particle swarm' s iterative process, It uses chaotic map to guide swarms for chaotic search,it makes the algorithm out of local optimum, and expands the ability of finding global optimal solution. Experimental results show that the proposed new classification algorithm for Chinese text classification is effective, the classification accuracy and recall are better than KNN algorithm.

  19. Clinical Supervision in Denmark

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    Data fra den danske undersøgelse af psykoterapeuters faglige udvikling indsamlet ved hjælp af DPCCQ. Oplægget fokuserer på supervision (modtaget, givet, uddannelse i) blandt danske psykoterapeutiske arbejdende psykologer....

  20. Supervision af psykoterapi

    DEFF Research Database (Denmark)

    SUPERVISION AF PSYKOTERAPI indtager en central position i uddannelsen og udviklingen af psykoterapeuter. Trods flere lighedspunkter med psykoterapi, undervisning og konsultation er psykoterapisupervision et selvstændigt virksomhedsområde. Supervisor må foruden at være en trænet psykoterapeut kende...... supervisionens rammer og indplacering i forhold til organisation og samfund. En række kapitler drejer sig om supervisors opgaver, roller og kontrolfunktion, supervision set fra supervisandens perspektiv samt betragtninger over relationer og processer i supervision. Der drøftes fordele og ulemper ved de...... forskellige måder, hvorpå en sag kan fremlægges. Bogens første del afsluttes med refleksioner over de etiske aspekter ved psykoterapisupervision. Bogens anden del handler om de særlige forhold, der gør sig gældende ved supervision af en række specialiserede behandlingsformer eller af psykoterapi med bestemte...

  1. Psykoterapi og supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2014-01-01

    Kapitlet beskriver supervisionen funktioner i forhold til psykoterapi. Supervision af psykoterapi henviser i almindelighed til, at en psykoterapeut konsulterer en ofte mere erfaren kollega (supervisor) med henblik på drøftelse af et konkret igangværende psykoterapeutisk behandlingsforløb. Formålet...... er at fremme denne fagpersons (psykoterapeutens) faglige udvikling samt sikre kvaliteten af behandlingen.kan defineres som i. Der redegøres for, hvorfor supervision er vigtig del af psykoterapeutens profession samt vises, hvorledes supervision foruden den faglige udvikling også er vigtigt redskab i...... psykoterapiens kvalitetssikring. Efter at have drøftet nogle etiske forhold ved supervision, fremlægges endelig nogle få forskningsresultater vedr. psykoterapisupervision af danske psykologer....

  2. Supervision and group dynamics

    DEFF Research Database (Denmark)

    Hansen, Søren; Jensen, Lars Peter

    2004-01-01

    as well as at Aalborg University. The first visible result has been participating supervisors telling us that the course has inspired them to try supervising group dynamics in the future. This paper will explore some aspects of supervising group dynamics as well as, how to develop the Aalborg model...... An important aspect of the problem based and project organized study at Aalborg University is the supervision of the project groups. At the basic education (first year) it is stated in the curriculum that part of the supervisors' job is to deal with group dynamics. This is due to the experience...... that many students are having difficulties with practical issues such as collaboration, communication, and project management. Most supervisors either ignore this demand, because they do not find it important or they find it frustrating, because they do not know, how to supervise group dynamics...

  3. Supervised Classification Performance of Multispectral Images

    CERN Document Server

    Perumal, K

    2010-01-01

    Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the increasing spatiotemporal dimensions of the remote sensing data, traditional classification algorithms have exposed weaknesses necessitating further research in the field of remote sensing image classification. So an efficient classifier is needed to classify the remote sensing images to extract information. We are experimenting with both supervised and unsupervised classification. Here we compare the different classification methods and their performances. It is found that Mahalanobis classifier performed the best in our...

  4. Two Approaches to Clinical Supervision.

    Science.gov (United States)

    Anderson, Eugene M.

    Criteria are established for a definition of "clinical supervision" and the effectiveness of such supervisory programs in a student teaching context are considered. Two differing genres of clinical supervision are constructed: "supervision by pattern analysis" is contrasted with "supervision by performance objectives." An outline of procedural…

  5. Counselor Supervision: A Consumer's Guide.

    Science.gov (United States)

    Yager, Geoffrey G.; Littrell, John M.

    This guide attempts to solve problems caused when a certain designated "brand" of supervision is forced on the counselor trainee with neither choice nor checklist of important criteria. As a tentative start on a guide to supervision the paper offers the following: a definition of supervision; a summary of the various types of supervision; a…

  6. Principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis of colonic mucosal tissue fluorescence spectra.

    Science.gov (United States)

    Kamath, Sudha D; Mahato, Krishna K

    2009-08-01

    The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match/No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325 nm pulsed laser excitation in the spectral region 350-600 nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. The results obtained in this study showed good discrimination between normal and malignant conditions using PCA-based k-NN analysis.

  7. A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION

    Directory of Open Access Journals (Sweden)

    Nima Khairdoost

    2015-03-01

    Full Text Available Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA and Linear Discriminant Analysis (LDA are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA and cosine similarity-based K Nearest Neighbor (KNN classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.

  8. Estimation of the activation energy of sintering in KNN ceramics using master sintering theory

    Science.gov (United States)

    Singh, Rajan; Patro, P. K.; Kulkarni, Ajit R.; Harendranath, C. S.

    2014-04-01

    The master sintering curve (MSC) of K0.5Na0.5NbO3 (KNN) ceramics was constructed using constant heating rate dilatometry data based on the combined stage sintering model. The linear shrinkage was recorded using three heating rates 5 °C, 7 °C and 11 °C/ min. The obtained results suggest that in MSC, the sintered density is a unique function of the integral of a temperature function over time and it is independent of the sintering history. The MSC theory can be applied to predict shrinkage and final density. Also, it can be used to design a reproducible process to fabricate ceramics with required density.

  9. The First-Principle Calculation of La-doping Effect on Piezoelectricity in Tetragonal KNN Crystal

    Institute of Scientific and Technical Information of China (English)

    张乔丽; 朱基亮; 袁大庆; 朱波; 王明松; 朱小红; 范平; 左翼; 郑永男; 朱升云

    2012-01-01

    The La-dopping effect on the piezoelectricity in the K0.5Na0.5NbO3 (KNN) crystal with a tetragonal phase is investigated for the first time using the first-principle calculation based on density functional theory. The full potentiallinearized augumented plane wave plus local orbitals (APW-LO) method and the supercell method are used in the calculation for the KNN crystal with and without the La doping. The results show that the piezoelectricity originates from the strong hybridization between the Nb atom and the O atom, and the substitution of the K or Na atom by the La impurity atom introduces the anisotropic relaxation and enhances the piezoelectricity at first and then restrains the hybridization of the Nb-O atoms when the La doping content further increases.

  10. The high density phase of the k-NN hard core lattice gas model

    Science.gov (United States)

    Nath, Trisha; Rajesh, R.

    2016-07-01

    The k-NN hard core lattice gas model on a square lattice, in which the first k next nearest neighbor sites of a particle are excluded from being occupied by another particle, is the lattice version of the hard disc model in two dimensional continuum. It has been conjectured that the lattice model, like its continuum counterpart, will show multiple entropy-driven transitions with increasing density if the high density phase has columnar or striped order. Here, we determine the nature of the phase at full packing for k up to 820 302 . We show that there are only eighteen values of k, all less than k  =  4134, that show columnar order, while the others show solid-like sublattice order.

  11. (100)-Textured KNN-based thick film with enhanced piezoelectric property for intravascular ultrasound imaging

    Science.gov (United States)

    Zhu, Benpeng; Zhang, Zhiqiang; Ma, Teng; Yang, Xiaofei; Li, Yongxiang; Shung, K. Kirk; Zhou, Qifa

    2015-04-01

    Using tape-casting technology, 35 μm free-standing (100)-textured Li doped KNN (KNLN) thick film was prepared by employing NaNbO3 (NN) as template. It exhibited similar piezoelectric behavior to lead containing materials: a longitudinal piezoelectric coefficient (d33) of ˜150 pm/V and an electromechanical coupling coefficient (kt) of 0.44. Based on this thick film, a 52 MHz side-looking miniature transducer with a bandwidth of 61.5% at -6 dB was built for Intravascular ultrasound (IVUS) imaging. In comparison with 40 MHz PMN-PT single crystal transducer, the rabbit aorta image had better resolution and higher noise-to-signal ratio, indicating that lead-free (100)-textured KNLN thick film may be suitable for IVUS (>50 MHz) imaging.

  12. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  13. Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

    Science.gov (United States)

    Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

    2016-03-01

    Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

  14. Improved semi-supervised online boosting for object tracking

    Science.gov (United States)

    Li, Yicui; Qi, Lin; Tan, Shukun

    2016-10-01

    The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

  15. Brut: Automatic bubble classifier

    Science.gov (United States)

    Beaumont, Christopher; Goodman, Alyssa; Williams, Jonathan; Kendrew, Sarah; Simpson, Robert

    2014-07-01

    Brut, written in Python, identifies bubbles in infrared images of the Galactic midplane; it uses a database of known bubbles from the Milky Way Project and Spitzer images to build an automatic bubble classifier. The classifier is based on the Random Forest algorithm, and uses the WiseRF implementation of this algorithm.

  16. Resistance to group clinical supervision

    DEFF Research Database (Denmark)

    Buus, Niels; Delgado, Cynthia; Traynor, Michael

    2017-01-01

    This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has traditiona......This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has...... traditionally been theorized as a supervisee's maladaptive coping with anxiety in the supervision process. The aim of the present study was to examine resistance to group clinical supervision by interviewing nurses who did not participate in supervision. In 2015, we conducted semistructured interviews with 24...

  17. Detection and localization of myocardial infarction using K-nearest neighbor classifier.

    Science.gov (United States)

    Arif, Muhammad; Malagore, Ijaz A; Afsar, Fayyaz A

    2012-02-01

    This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

  18. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images.

    Science.gov (United States)

    Gopinath, Balasubramanian; Shanthi, Natesan

    2013-06-01

    An automated computer-aided diagnosis system is developed to classify benign and malignant thyroid nodules using multi-stained fine needle aspiration biopsy (FNAB) cytological images. In the first phase, the image segmentation is performed to remove the background staining information and retain the appropriate foreground cell objects in cytological images using mathematical morphology and watershed transform segmentation methods. Subsequently, statistical features are extracted using two-level discrete wavelet transform (DWT) decomposition, gray level co-occurrence matrix (GLCM) and Gabor filter based methods. The classifiers k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) are tested for classifying benign and malignant thyroid nodules. The combination of watershed segmentation, GLCM features and k-NN classifier results a lowest diagnostic accuracy of 60 %. The highest diagnostic accuracy of 93.33 % is achieved by ENN classifier trained with the statistical features extracted by Gabor filter bank from the images segmented by morphology and watershed transform segmentation methods. It is also observed that SVM classifier results its highest diagnostic accuracy of 90 % for DWT and Gabor filter based features along with morphology and watershed transform segmentation methods. The experimental results suggest that the developed system with multi-stained thyroid FNAB images would be useful for identifying thyroid cancer irrespective of staining protocol used.

  19. Collective academic supervision

    DEFF Research Database (Denmark)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    are interconnected. Collective Academic Supervision provides possibilities for systematic interaction between individual master students in their writing process. In this process they learn core academic competencies, such as the ability to assess theoretical and practical problems in their practice and present them...

  20. Reflecting reflection in supervision

    DEFF Research Database (Denmark)

    Lystbæk, Christian Tang

    Reflection has moved from the margins to the mainstream in supervision. Notions of reflection have become well established since the late 1980s. These notions have provided useful framing devices to help conceptualize some important processes in guidance and counseling. However, some applications...

  1. Clinical Supervision in Denmark

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2011-01-01

    on giving and receiving clinical supervision as reported by therapists in Denmark. Method: Currently, the Danish sample consists of 350 clinical psychologist doing psychotherapy who completed DPCCQ. Data are currently being prepared for statistical analysis. Results: This paper will focus primarily...

  2. Kontraktetablering i supervision

    DEFF Research Database (Denmark)

    Mortensen, Karen Vibeke; Jacobsen, Claus Haugaard

    2007-01-01

    Kapitlet behandler kontraktetablering i supervision, et element, der ofte er blevet negligeret eller endog helt forbigået ved indledningen af supervisionsforløb. Sikre aftaler om emner som tid, sted, procedurer for fremlæggelse, fortrolighed, ansvarsfordeling og evaluering skaber imidlertid tryghed...

  3. Etiske betragtninger ved supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard; Agerskov, Kirsten

    2007-01-01

    Kapitlet præsenterer nogle etiske betragtninger ved supervision. Mens der længe har eksisteret etiske retningslinjer for psykoterapeutisk arbejde, har der overraskende nok manglet tilsvarende vejledninger på supervisionsområdet. Det betyder imidlertid ikke, at de ikke er relevante. I kapitlet gøres...

  4. Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery

    Directory of Open Access Journals (Sweden)

    Hayder Dibs

    2017-06-01

    Full Text Available There has been growing interest in Malaysia to increase the productivity of latex. This made accurate knowledge of rubber tree growth and age distribution a helpful decision making tool for the government, rubber plantation managers, and harvesters. Gathering this information using conventional methods is difficult, time consuming, and limited in spatial coverage. This paper presents hierarchical classification approach to obtain accurate map of rubber tree growth age distribution using SPOT-5 satellite imagery. The objective of the study is to evaluate the performance of pixel-based and object-oriented classifiers for rubber growth classification. At the first level, the general land cover was classified into eight land cover classes (soil, water body, rubber, mature oil palm, young oil palm, forest, urban area, and other vegetation using Mahalanobis distance (MD, k-nearest neighbor (k-NN, and Support Vector Machine (SVM classifiers. Thereafter, the best classification map, k-NN output, was used to select only pixels that belong to the rubber class from the SPOT-5 image. The extracted pixels served as input into the next classification hierarchy where four classifiers, MD, k-NN, SVM, and decision tree (DT, were implemented to map rubber trees into three intra-classes (mature, middle-aged, and young rubbers. The result produced overall accuracy of 97.48%, 96.90%, 96.25%, and 80.80% for k-NN, SVM, MD, and DT respectively. The result indicates that object-oriented classifiers are better than pixel-based methods mapping rubber tree growth.

  5. Dynamic system classifier

    Science.gov (United States)

    Pumpe, Daniel; Greiner, Maksim; Müller, Ewald; Enßlin, Torsten A.

    2016-07-01

    Stochastic differential equations describe well many physical, biological, and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time-dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of the DSC to oscillation processes with a time-dependent frequency ω (t ) and damping factor γ (t ) . Although real systems might be more complex, this simple oscillator captures many characteristic features. The ω and γ time lines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiments show that such classifiers perform well even in the low signal-to-noise regime.

  6. Algorithm of Identifying Differentially Methylated Region Based on Sliding Windows and KNN%利用滑动窗口和 KNN 算法识别差异甲基化区域

    Institute of Scientific and Technical Information of China (English)

    李华兵; 杨昆

    2016-01-01

    针对现有差异甲基化区域 DMRs 识别方法中过度删除显著性弱的甲基化位点、DMRs 长度受限以及不能直接处理多类的问题,提出了一种利用滑动窗口和 KNN 算法识别不同类别间DMRs 的算法.算法先通过滑动窗口结合 KNN 分类器筛选候选区域,再根据误差率合并候选区域得到 DMRs.真实数据上的实验表明,算法的分类性能、聚类指数明显优于对照算法,扩展了对照的 Ong 算法识别的 DMRs 长度,并能发现 Ong 算法未发现的 DMRs.%In view of the shortcomings of the existing methods for identifying differentially methylated regions(DMRs),such as over deletion of sites that significance are weaker,region length limitation and can’t be directly processed by the multi-class.An algorithm of identifying DMRs based on sliding window and k-nearest neighbor(KNN)is proposed.In this method,candidate regions are obtained using sliding windows and KNN,and it merges candidate regions to get DMRs.Through real data simulation results demonstrate the method is superior to control method, such as classification performance,cluster index,the DMRs length of the control methods of Ong is extended and find some DMRs that can’t be found in control algorithm of Ong.

  7. Bias Modeling for Distantly Supervised Relation Extraction

    Directory of Open Access Journals (Sweden)

    Yang Xiang

    2015-01-01

    Full Text Available Distant supervision (DS automatically annotates free text with relation mentions from existing knowledge bases (KBs, providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP. However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1 bias-dist to model the relative distance between points (instances and classes (relation centers; (2 bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.

  8. Optical and Piezoelectric Study of KNN Solid Solutions Co-Doped with La-Mn and Eu-Fe

    Directory of Open Access Journals (Sweden)

    Jesús-Alejandro Peña-Jiménez

    2016-09-01

    Full Text Available The solid-state method was used to synthesize single phase potassium-sodium niobate (KNN co-doped with the La3+–Mn4+ and Eu3+–Fe3+ ion pairs. Structural determination of all studied solid solutions was accomplished by XRD and Rietveld refinement method. Electron paramagnetic resonance (EPR studies were performed to determine the oxidation state of paramagnetic centers. Optical spectroscopy measurements, excitation, emission and decay lifetime were carried out for each solid solution. The present study reveals that doping KNN with La3+–Mn4+ and Eu3+–Fe3+ at concentrations of 0.5 mol % and 1 mol %, respectively, improves the ferroelectric and piezoelectric behavior and induce the generation of optical properties in the material for potential applications.

  9. Prospects of using the k-NN method of classification of satellite images for the forest inventory in Ukraine

    OpenAIRE

    V. Myroniuk

    2015-01-01

    This paper deals with modern experience of statistical inventory of forests using ground-based inventory and remote sensing data (RSD). A detailed analysis of the k-NN method of classification of satellite images is given and features of its applying for thematic mapping of forest fund under the statistical forest inventory defined. The algorithm for calculating the stock of plantings for the statistical software with R open source is shown on the example of local research material.

  10. QSAR analysis of furanone derivatives as potential COX-2 inhibitors: kNN MFA approach

    Directory of Open Access Journals (Sweden)

    Ruchi Bhatiya

    2014-12-01

    Full Text Available A series of thirty-two furanone derivatives with their cyclooxygenase-2 inhibitory activity were subjected to quantitative structural–activity relationship analysis to derive a correlation between biological activity as a dependent variable and various descriptors as independent variables by using V-LIFE MDS3.5 software. The significant 2D QSAR model showed correlation coefficient (r2 = 0.840, standard error of estimation (SEE = 0.195, and a cross-validated squared correlation coefficient (q2 = 0.773. The descriptors involved in the building of 2D QSAR model are retention index for six membered rings, total number of oxygen connected with two single bonds, polar surface area excluding P and S plays a significant role in COX-2 inhibition. 3D-QSAR performed via Step Wise K Nearest Neighbor Molecular Field Analysis [(SW kNN MFA] with partial least-square (PLS technique showed high predictive ability (r2 = 0.7622, q2 = 0.7031 and standard error = 0.3660 explaining the majority of the variance in the data with two principle components. The results of the present study may be useful in the design of more potent furanone derivatives as COX-2 inhibitors.

  11. Classifying Similarity and Defect Fabric Textures based on GLCM and Binary Pattern Schemes

    Directory of Open Access Journals (Sweden)

    R. Obula Konda Reddy

    2013-11-01

    Full Text Available Textures are one of the basic features in visual searching,computational vision and also a general property of any surface having ambiguity. This paper presents a texture classification system which has high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM and binary pattern based automated similarity identification and defect detection model is presented. Different features are calculated from both GLCM and binary patterns (LBP, LLBP, and SLBP. Then a new rotation-invariant, scale invariant steerable decomposition filter is applied to filter the four orientation sub bands of the image. The experimental results are evaluated and a comparative analysis has been performed for the four different feature types. Finally the texture is classified by different classifiers (PNN, K-NN and SVM and the classification performance of each classifier is compared. The experimental results have shown that the proposed method produces more accuracy and better classification accuracy over other methods.

  12. The measurement of KNN,KLLINpd -->nX and p9Be-->nX at 800 MeV

    Science.gov (United States)

    Riley, P. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Bonner, B. E.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.

    1981-07-01

    The polarization transfer parameters,KNN and KLL, have been measured in pd-->nX and p9Be-->nX at 0° and 800 MeV. The quasifree pd-->nX values for KNN and KLL are close to the free n-p measured values. The rather large values of KLL demonstrate that this transfer mechanism will provide a useful source of polarized neutrons at medium energies.

  13. Experimenting the design-based k-NN approach for mapping and estimation under forest management planning

    Directory of Open Access Journals (Sweden)

    Mattioli W

    2012-02-01

    Full Text Available Estimation and mapping of forest attributes are a fundamental support for forest management planning. This study describes a practical experimentation concerning the use of design-based k-Nearest Neighbors (k-NN approach to estimate and map selected attributes in the framework of inventories at forest management level. The study area was the Chiarino forest within the Gran Sasso and Monti della Laga National Park (central Italy. Aboveground biomass and current annual increment of tree volume were selected as the attributes of interest for the test. Field data were acquired within 28 sample plots selected by stratified random sampling. Satellite data were acquired by a Landsat 5 TM multispectral image. Attributes from field surveys and Landsat image processing were coupled by k-NN to predict the attributes of interest for each pixel of the Landsat image. Achieved results demonstrate the effectiveness of the k-NN approach for statistical estimation, that is compatible with the produced forest attribute raster maps and also proves to be characterized, in the considered study case, by a precision double than that obtained by conventional inventory based on field sample plots only.

  14. Social networks in supervision

    DEFF Research Database (Denmark)

    Lystbæk, Christian Tang

    and practice have focused on conceptual frameworks and practical techniques of promoting reflection through conversation in general and questioning in particular. However, in recent years, supervision research has started to focus on the social and technological aspects of supervision. This calls...... is constituted by the relationality of the actors, not by the actors themselves. In other words, no one acts in a vacuum but rather always under the influence of a wide range of surrounding and interconnected factors. Actors are actors because they are in a networked relationship. Thus, focusing on social...... and space. That involves mobilised an denrolled actos, both animate and inanimate (e.g. books, computers, etc. Actor-network theory defines a symmetry between animate and inanimate, i.e. subjects and objects, because ”human powers increasingly derive from the complex interconnections if human with material...

  15. Dynamic system classifier

    CERN Document Server

    Pumpe, Daniel; Müller, Ewald; Enßlin, Torsten A

    2016-01-01

    Stochastic differential equations describe well many physical, biological and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of DSC to oscillation processes with a time dependent frequency {\\omega}(t) and damping factor {\\gamma}(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The {\\omega} and {\\gamma} timelines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiment...

  16. Ethics in education supervision

    Directory of Open Access Journals (Sweden)

    Fatma ÖZMEN

    2008-06-01

    Full Text Available Supervision in education plays a crucial role in attaining educational goals. In addition to determining the present situation, it has a theoretical and practical function regarding the actions to be taken in general and the achievement of teacher development in particular to meet the educational goals in the most effective way. For the education supervisors to act ethically in their tasks while achieving this vital mission shall facilitate them to build up trust, to enhance the level of collaboration and sharing, thus it shall contribute to organizational effectiveness. Ethics is an essential component of educational supervision. Yet, it demonstrates rather vague quality due to the conditions, persons, and situations. Therefore, it is a difficult process to develop the ethical standards in institutions. This study aims to clarify the concept of ethics, to bring up its importance, and to make recommendations for more effective supervisions from the aspect of ethics, based on the literature review, some research results, and sample cases reported by teachers and supervisors.

  17. Classifying Returns as Extreme

    DEFF Research Database (Denmark)

    Christiansen, Charlotte

    2014-01-01

    I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...

  18. Visual Classifier Training for Text Document Retrieval.

    Science.gov (United States)

    Heimerl, F; Koch, S; Bosch, H; Ertl, T

    2012-12-01

    Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.

  19. LCC: Light Curves Classifier

    Science.gov (United States)

    Vo, Martin

    2017-08-01

    Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

  20. An Efficient Refining Of Cbir through Supervised Learning Approach

    Directory of Open Access Journals (Sweden)

    R. Bindhu

    2014-03-01

    Full Text Available CBIR(Content Based Image Retrieval technique has its own importance in medical field to store, manage, and retrieve data images based on user query. Here we propose a framework based on design and development of a multi-tier Content-Based Image Retrieval system for MRI brain images utilizing a reference database that contains both normal and tumor brain images under the category which it falls(i.e. normal, benign or malignant tumor ,with their identity number, which are mostly difficult to classify and discriminate. The features of the image are extracted using gray level co-occurrence matrix (GLCM technique and a subset of features is selected using Differential Evolution Feature Selection (DEFS technique. The selected features are sent through the classifier (SVM. Searching is done by means of matching the image features such as texture, shape, or different combinations of them. SVM (Support Vector machine classifier followed by KNN (K-nearest neighbor for CBIR using texture and shape feature.This CBIR system enables both multi-image query and slide-level image retrieval in order to protect semantic consistency among the retrieved images. The performance of the system is tested on the dataset by several MRI brain images of various categories, and the features of the image in the dataset matching more accurately of the features of query images are listed as retrieved images with their identification number for better accuracy.

  1. Researching online supervision

    DEFF Research Database (Denmark)

    Smedegaard Ernst Bengtsen, Søren; Mathiasen, Helle

    2014-01-01

    , or a poor substitution of such. This one-sidedness on the conceptual level makes it challenging to empirically study the deeper implications digital tools have for the supervisory dialogue. Drawing on phenomenology and systems theory we argue that we need new concepts in qualitative methodology that allow...... us to research the digital tools on their own premises as autonomous things in themselves, possessing an ontological creativity of their own. In order for qualitative research to match the ontological nature of digital tools we conclude the article by formulating three criteria of a ‘torn......’ methodology that makes room for new approaches to researching online supervision at the university....

  2. Researching online supervision

    DEFF Research Database (Denmark)

    Bengtsen, Søren S. E.; Mathiasen, Helle

    2014-01-01

    us to research the digital tools on their own premises as autonomous things in themselves, possessing an ontological creativity of their own. In order for qualitative research to match the ontological nature of digital tools we conclude the article by formulating three criteria of a ‘torn......’ methodology that makes room for new approaches to researching online supervision at the university......., or a poor substitution of such. This one-sidedness on the conceptual level makes it challenging to empirically study the deeper implications digital tools have for the supervisory dialogue. Drawing on phenomenology and systems theory we argue that we need new concepts in qualitative methodology that allow...

  3. A Supervised Classification Algorithm for Note Onset Detection

    Directory of Open Access Journals (Sweden)

    Douglas Eck

    2007-01-01

    Full Text Available This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.

  4. Classifier in Age classification

    Directory of Open Access Journals (Sweden)

    B. Santhi

    2012-12-01

    Full Text Available Face is the important feature of the human beings. We can derive various properties of a human by analyzing the face. The objective of the study is to design a classifier for age using facial images. Age classification is essential in many applications like crime detection, employment and face detection. The proposed algorithm contains four phases: preprocessing, feature extraction, feature selection and classification. The classification employs two class labels namely child and Old. This study addresses the limitations in the existing classifiers, as it uses the Grey Level Co-occurrence Matrix (GLCM for feature extraction and Support Vector Machine (SVM for classification. This improves the accuracy of the classification as it outperforms the existing methods.

  5. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  6. Semi-supervised SVM for individual tree crown species classification

    Science.gov (United States)

    Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

    2015-12-01

    In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

  7. Classifying Linear Canonical Relations

    OpenAIRE

    Lorand, Jonathan

    2015-01-01

    In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

  8. Intelligent Garbage Classifier

    Directory of Open Access Journals (Sweden)

    Ignacio Rodríguez Novelle

    2008-12-01

    Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

  9. Online supervision at the university

    DEFF Research Database (Denmark)

    Bengtsen, Søren Smedegaard; Jensen, Gry Sandholm

    2015-01-01

    The article presents and condenses the background, findings and results of a one yearlong research project on online supervision and feedback at the university. The article builds on presentations and discussions in different research environments and conferences on higher education research...... supervision proves unhelpful when trying to understand how online supervision and feedback is a pedagogical phenomenon in its own right, and irreducible to the face-to-face context. Secondly we show that not enough attention has been given to the way different digital tools and platforms influence...... the supervisory dialogue in the specific supervision context. We conclude by terming this challenge in online supervision a form of ‘torn pedagogy’; that online tools and platforms destabilise and ‘tear’ traditional understandings of supervision pedagogy ‘apart’. Also, we conclude that on the backdrop of a torn...

  10. Generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2013-03-01

    In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.

  11. KNN结合PCA在激光诱导荧光光谱识别矿井突水中的应用%Application of the Identification of Mine Water Inrush with LIF Spectrometry and KNN Algorithm Combined with PCA

    Institute of Scientific and Technical Information of China (English)

    何晨阳; 周孟然; 闫鹏程

    2016-01-01

    Rapid identification and classification of mine water inrush is important for flood prevention work underground .This paper proposed a method of KNN combined with PCA identification of water inrush in mine with the laser induced fluorescence spectrum with an immersion probe laser into water samples .The fluorescence spectra of 4 kinds of water samples were obtained . For each set of data preprocessing ,the processed data in each sample from 15 sets of data as the training setwith a total of 60 groups .The other 20 groups were used as the prediction set .The data were processed by principal component analysis (PCA) , and then the KNN algorithm was used to classify and identify the principal component analysis .During the experiment ,the pre‐treatment method in the principal component number is 2 while the correct rate has reached 100% by KNN classification algo‐rithm .%矿井突水的迅速识别与分类对于井下水灾防治工作有着重要的意义。提出一种KNN结合PCA运用在激光诱导荧光光谱快速识别矿井突水水源中的新方法。利用激光器发射激光通过可浸入式探头射入水样,得到四种突水水样共80组荧光光谱数据,再分别对每组数据进行预处理,处理后的数据中每种水样取15组数据作为训练集,共60组,其余20组作为预测集。利用主成分分析(PCA)对数据进行处理,之后在主成分分析的基础上利用KNN算法进行分类识别。实验过程中,各预处理方法在主成分个数为2的情况下,进行KNN算法分类的正确率都达到100%。

  12. Generative supervised classification using Dirichlet process priors.

    Science.gov (United States)

    Davy, Manuel; Tourneret, Jean-Yves

    2010-10-01

    Choosing the appropriate parameter prior distributions associated to a given bayesian model is a challenging problem. Conjugate priors can be selected for simplicity motivations. However, conjugate priors can be too restrictive to accurately model the available prior information. This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of Dirichlet processes. The motivations for using mixtures of Dirichlet processes is their known ability to model accurately a large class of probability distributions. A Monte Carlo method allowing one to sample according to the resulting class-conditional posterior distributions is then studied. The parameters appearing in the class-conditional densities can then be estimated using these generated samples (following bayesian learning). The proposed supervised classifier is applied to the classification of altimetric waveforms backscattered from different surfaces (oceans, ices, forests, and deserts). This classification is a first step before developing tools allowing for the extraction of useful geophysical information from altimetric waveforms backscattered from nonoceanic surfaces.

  13. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  14. Advanced Music Therapy Supervision Training

    DEFF Research Database (Denmark)

    2009-01-01

    supervision training excerpts live in the workshop will be offered. The workshop will include demonstrating a variety of supervision methods and techniques used in A) post graduate music therapy training programs b) a variety of work contexts such as psychiatry and somatic music psychotherapy. The workshop......The presentation will illustrate training models in supervision for experienced music therapists where transference/counter transference issues are in focus. Musical, verbal and body related tools will be illustrated from supervision practice by the presenters. A possibility to experience small...

  15. Advanced Music Therapy Supervision Training

    DEFF Research Database (Denmark)

    2009-01-01

    supervision training excerpts live in the workshop will be offered. The workshop will include demonstrating a variety of supervision methods and techniques used in A) post graduate music therapy training programs b) a variety of work contexts such as psychiatry and somatic music psychotherapy. The workshop......The presentation will illustrate training models in supervision for experienced music therapists where transference/counter transference issues are in focus. Musical, verbal and body related tools will be illustrated from supervision practice by the presenters. A possibility to experience small...

  16. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  17. Public Supervision over Private Relationships : Towards European Supervision Private Law?

    NARCIS (Netherlands)

    Cherednychenko, O.O.

    2014-01-01

    The rise of public supervision over private relationships in many areas of private law has led to the development of what, in the author’s view, could be called ‘European supervision private law’. This emerging body of law forms part of European regulatory private law and is made up of contract-rela

  18. Supervising PETE Candidates Using the Situational Supervision Model

    Science.gov (United States)

    Levy, Linda S.; Johnson, Lynn V.

    2012-01-01

    Physical education teacher candidates (PETCs) often, as part of their curricular requirements, engage in early field experiences that prepare them for student teaching. Matching the PETC's developmental level with the mentor's supervision style enhances this experience. The situational supervision model, based on the situational leadership model,…

  19. Exploring Clinical Supervision as Instrument for Effective Teacher Supervision

    Science.gov (United States)

    Ibara, E. C.

    2013-01-01

    This paper examines clinical supervision approaches that have the potential to promote and implement effective teacher supervision in Nigeria. The various approaches have been analysed based on the conceptual framework of instructional supervisory behavior. The findings suggest that a clear distinction can be made between the prescriptive and…

  20. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  1. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  2. Supervision of Supervised Agricultural Experience Programs: A Synthesis of Research.

    Science.gov (United States)

    Dyer, James E.; Williams, David L.

    1997-01-01

    A review of literature from 1964 to 1993 found that supervised agricultural experience (SAE) teachers, students, parents, and employers value the teachers' supervisory role. Implementation practices vary widely and there are no cumulative data to guide policies and standards for SAE supervision. (SK)

  3. High Performance Medical Classifiers

    Science.gov (United States)

    Fountoukis, S. G.; Bekakos, M. P.

    2009-08-01

    In this paper, parallelism methodologies for the mapping of machine learning algorithms derived rules on both software and hardware are investigated. Feeding the input of these algorithms with patient diseases data, medical diagnostic decision trees and their corresponding rules are outputted. These rules can be mapped on multithreaded object oriented programs and hardware chips. The programs can simulate the working of the chips and can exhibit the inherent parallelism of the chips design. The circuit of a chip can consist of many blocks, which are operating concurrently for various parts of the whole circuit. Threads and inter-thread communication can be used to simulate the blocks of the chips and the combination of block output signals. The chips and the corresponding parallel programs constitute medical classifiers, which can classify new patient instances. Measures taken from the patients can be fed both into chips and parallel programs and can be recognized according to the classification rules incorporated in the chips and the programs design. The chips and the programs constitute medical decision support systems and can be incorporated into portable micro devices, assisting physicians in their everyday diagnostic practice.

  4. Classifying Facial Actions

    Science.gov (United States)

    Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

    2010-01-01

    The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

  5. Inductive Supervised Quantum Learning

    Science.gov (United States)

    Monràs, Alex; Sentís, Gael; Wittek, Peter

    2017-05-01

    In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

  6. Supervision in Special Language Programs.

    Science.gov (United States)

    Florez-Tighe, Viola

    Too little emphasis is placed on instructional supervision in special language programs for limited-English-proficient students. Such supervision can provide a mechanism to promote the growth of instructional staff, improve the instructional program, and lead to curriculum development. Many supervisors are undertrained and unable to provide…

  7. Unfinished Business: Subjectivity and Supervision

    Science.gov (United States)

    Green, Bill

    2005-01-01

    Within the now burgeoning literature on doctoral research education, postgraduate research supervision continues to be a problematical issue, practically and theoretically. This paper seeks to explore and understand supervision as a distinctive kind of pedagogic practice. Informed by a larger research project, it draws on poststructuralism,…

  8. Supervision af psykoterapi via Skype

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard; Grünbaum, Liselotte

    2011-01-01

    clinical experience of Skype™ in supervision, mainly of psychoanalytic child psychotherapy, is presented and reflected upon. Finally, the reluctance of the Danish Board for Psychologists’s to recognize audiovisual distance supervision as part of the required training demands is discussed. It is concluded...

  9. Supervisees' Perception of Clinical Supervision

    Science.gov (United States)

    Willis, Lisa

    2010-01-01

    Supervisors must become aware of the possible conflicts that could arise during clinical supervision. It is important that supervisors communicate their roles and expectations effectively with their supervisees. This paper supports the notion that supervision is a mutual agreement between the supervisee and the supervisor and the roles of…

  10. Assessment of Counselors' Supervision Processes

    Science.gov (United States)

    Ünal, Ali; Sürücü, Abdullah; Yavuz, Mustafa

    2013-01-01

    The aim of this study is to investigate elementary and high school counselors' supervision processes and efficiency of their supervision. The interview method was used as it was thought to be better for realizing the aim of the study. The study group was composed of ten counselors who were chosen through purposeful sampling method. Data were…

  11. Tværfaglig supervision

    DEFF Research Database (Denmark)

    Tværfaglig supervision dækker over supervision af forskellige faggrupper. Det er en kompleks disciplin der stiller store krav tl supervisor. Bogens første del præsenterer fire faglige supervisionsmodeller: En almen, en psykodynamisk, en kognitiv adfærdsterapeutisk og en narrativ. Anden del...

  12. Tværfaglig supervision

    DEFF Research Database (Denmark)

    Tværfaglig supervision dækker over supervision af forskellige faggrupper. Det er en kompleks disciplin der stiller store krav tl supervisor. Bogens første del præsenterer fire faglige supervisionsmodeller: En almen, en psykodynamisk, en kognitiv adfærdsterapeutisk og en narrativ. Anden del henven...

  13. Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; de Bakker, M.; Bouten, W.; Shamoun-Baranes, J.; Afsarmanesh, H.

    2012-01-01

    In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used

  14. Supervision Duty of School Principals

    Directory of Open Access Journals (Sweden)

    Kürşat YILMAZ

    2009-04-01

    Full Text Available Supervision by school administrators is becoming more and more important. The change in the roles ofschool administrators has a great effect on that increase. At present, school administrators are consideredmore than as technical directors, but as instructional leaders. This increased the importance of schooladministrators’ expected supervision acts. In this respect, the aim of this study is to make a conceptualanalysis about school administrators’ supervision duties. For this reason, a literature review related withsupervision and contemporary supervision approaches was done, and the official documents concerningsupervision were examined. As a result, it can be said that school administrators’ supervision duties havebecome very important. And these duties must certainly be carried out by school administrators.

  15. Group supervision for general practitioners

    DEFF Research Database (Denmark)

    Galina Nielsen, Helena; Sofie Davidsen, Annette; Dalsted, Rikke;

    2013-01-01

    AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long...... considered important prerequisites for disclosing and discussing professional problems. CONCLUSION: The results of this study indicate that participation in a supervision group can be beneficial for maintaining and developing GPs' skills in dealing with patients with mental health problems. Group supervision......-established supervision group was studied closely for six months by observing the group sessions, and by interviewing GPs and their supervisors, individually and collectively. The interviews were recorded digitally and transcribed verbatim. The data were analysed using systematic text condensation. RESULTS: The GPs found...

  16. Polarimetric SAR Image Supervised Classification Method Integrating Eigenvalues

    Directory of Open Access Journals (Sweden)

    Xing Yanxiao

    2016-04-01

    Full Text Available Since classification methods based on H/α space have the drawback of yielding poor classification results for terrains with similar scattering features, in this study, we propose a polarimetric Synthetic Aperture Radar (SAR image classification method based on eigenvalues. First, we extract eigenvalues and fit their distribution with an adaptive Gaussian mixture model. Then, using the naive Bayesian classifier, we obtain preliminary classification results. The distribution of eigenvalues in two kinds of terrains may be similar, leading to incorrect classification in the preliminary step. So, we calculate the similarity of every terrain pair, and add them to the similarity table if their similarity is greater than a given threshold. We then apply the Wishart distance-based KNN classifier to these similar pairs to obtain further classification results. We used the proposed method on both airborne and spaceborne SAR datasets, and the results show that our method can overcome the shortcoming of the H/α-based unsupervised classification method for eigenvalues usage, and produces comparable results with the Support Vector Machine (SVM-based classification method.

  17. Classifiers and Plurality: evidence from a deictic classifier language

    Directory of Open Access Journals (Sweden)

    Filomena Sandalo

    2016-12-01

    Full Text Available This paper investigates the semantic contribution of plural morphology and its interaction with classifiers in Kadiwéu. We show that Kadiwéu, a Waikurúan language spoken in South America, is a classifier language similar to Chinese but classifiers are an obligatory ingredient of all determiner-like elements, such as quantifiers, numerals, and wh-words for arguments. What all elements with classifiers have in common is that they contribute an atomized/individualized interpretation of the NP. Furthermore, this paper revisits the relationship between classifiers and number marking and challenges the common assumption that classifiers and plurals are mutually exclusive.

  18. Stack filter classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

    2009-01-01

    Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

  19. Classifying TDSS Stellar Variables

    Science.gov (United States)

    Amaro, Rachael Christina; Green, Paul J.; TDSS Collaboration

    2017-01-01

    The Time Domain Spectroscopic Survey (TDSS), a subprogram of SDSS-IV eBOSS, obtains classification/discovery spectra of point-source photometric variables selected from PanSTARRS and SDSS multi-color light curves regardless of object color or lightcurve shape. Tens of thousands of TDSS spectra are already available and have been spectroscopically classified both via pipeline and by visual inspection. About half of these spectra are quasars, half are stars. Our goal is to classify the stars with their correct variability types. We do this by acquiring public multi-epoch light curves for brighter stars (rSky Survey (CSS). We then run a number of light curve analyses from VARTOOLS, a program for analyzing astronomical time-series data, to constrain variable type both for broad statistics relevant to future surveys like the Transiting Exoplanet Survey Satellite (TESS) and the Large Synoptic Survey Telescope (LSST), and to find the inevitable exotic oddballs that warrant further follow-up. Specifically, the Lomb-Scargle Periodogram and the Box-Least Squares Method are being implemented and tested against their known variable classifications and parameters in the Catalina Surveys Periodic Variable Star Catalog. Variable star classifications include RR Lyr, close eclipsing binaries, CVs, pulsating white dwarfs, and other exotic systems. The key difference between our catalog and others is that along with the light curves, we will be using TDSS spectra to help in the classification of variable type, as spectra are rich with information allowing estimation of physical parameters like temperature, metallicity, gravity, etc. This work was supported by the SDSS Research Experience for Undergraduates program, which is funded by a grant from Sloan Foundation to the Astrophysical Research Consortium.

  20. Influence of different heat treatment programs on properties of sol–gel synthesized (Na0.5K0.5)NbO3 (KNN) thin films

    Indian Academy of Sciences (India)

    S Wiegand; S Flege; O Baake; W Ensinger

    2012-10-01

    Thin films of (Na0.5K0.5)NbO3 (KNN) were synthesized on Pt/Ti/SiO2/Si substrates with repeated spin-coating after fabrication of the precursor solution by a sol–gel process. The KNN precursor solution was prepared from K- and Na-acetate, Nb-pentaethoxide and 1,3-propanediol. Based on three characteristic temperatures derived from thermal analysis (TG–DTA) experiments, five heat treatment programs were developed. All programs lead to single phase perovskite KNN films with random crystal orientation, but only the programs that included a treatment after each single spin-coating step provided pore free surfaces with grains of about 100 nm size. The lowest leakage current at 150 kV cm-1 was obtained for the temperature program that included pyrolysis and calcination steps after each deposited layer.

  1. Study on Topic Tracking Based on KNN%基于KNN的话题跟踪研究

    Institute of Scientific and Technical Information of China (English)

    李树平; 夏春艳; 李胜东; 亓智斌; 赵杰

    2012-01-01

    The key technology of topic tracking task is text classification algorithm, its difficulty is topic / reports representation mod- el. According to the definition of topic tracking, contrast to commonly used text classification algorithms and text representation meth- ods, this paper selects KNN text classification algorithm as key technology of topic tracking, uses Topic vector space model to design topic / reports representation model, combines topic detection and tracking evaluation method to achieve the topic tracking system. Experimental results prove that the system has stable topic tracking performance when key technology of topic tracking is KNN.%话题跟踪任务的关键技术是文本分类算法,难点在于话题服道表示模型。根据话题跟踪的定义,对比常用的文本分类算法和文本表示方法,选择KNN文本分类算法作为话题跟踪关键技术,利用向量空间模型设计话题/报道表示模型,结合话题检测与跟踪评测方法实现了话题跟踪系统,试验结果证明KNN作为话题跟踪关键技术,系统具有较稳定的话题跟踪性能。

  2. An Approach to Indexing and Retrieval of Spatial Data with Reduced R+ Tree and K-NN Query Algorithm

    Directory of Open Access Journals (Sweden)

    S. Palaniappan

    2015-05-01

    Full Text Available Recently, “spatial data bases have been extensively adopted in the recent decade and various methods have been presented to store, browse, search and retrieve spatial objects”. In this study, a method is plotted for retrieving nearest neighbors from spatial data indexed by R+ tree. The approach uses a reduced R+tree for the purpose of representing the spatial data. Initially the spatial data is selected and R+tree is constructed accordingly. Then a function called joining nodes is applied to reduce the number of nodes by combining the half-filled nodes to form completely filled. The idea behind reducing the nodes is to perform search and retrieval quickly and efficiently. The reduced R+ tree is then processed with KNN query algorithm to fetch the nearest neighbors to a point query. The basic procedures of KNN algorithm are used in the proposed approach for retrieving the nearest neighbors. The proposed approach is evaluated for its performance withspatial data and results are plotted in the experimental analysis section. The experimental results showed that the proposed approach is remarkably up a head than the conventional methods. The maximum time required to index the 1000 data points by the R+ tree is 10324 ms. The number of nodes possessed by reduced R+ tree is also less for 1000 data points as compared to the conventional R+ tree algorithm.

  3. MScanner: a classifier for retrieving Medline citations

    Directory of Open Access Journals (Sweden)

    Altman Russ B

    2008-02-01

    Full Text Available Abstract Background Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. Results MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. Conclusion MScanner is an effective non-domain-specific classifier that operates on the entire Medline

  4. Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

    Energy Technology Data Exchange (ETDEWEB)

    Baraldi, Piero; Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico, E-mail: enrico.zio@polimi.it [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, Via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

    2011-05-15

    Research highlights: > A bagged ensemble of classifiers is applied for nuclear transient identification. > Fuzzy C-Means classifiers are used as base classifiers of the ensemble. > Transients are simulated in the feedwater system of a boiling water reactor. > Ensemble is compared with a supervised, evolutionary-optimized FCM classifier. > Ensemble improves classification accuracy in cases of large or very small sizes data. - Abstract: This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR).

  5. Semi Supervised Weighted K-Means Clustering for Multi Class Data Classification

    Directory of Open Access Journals (Sweden)

    Vijaya Geeta Dharmavaram

    2013-01-01

    Full Text Available Supervised Learning techniques require large number of labeled examples to train a classifier model. Research on Semi Supervised Learning is motivated by the availability of unlabeled examples in abundance even in domains with limited number of labeled examples. In such domains semi supervised classifier uses the results of clustering for classifier development since clustering does not rely only on labeled examples as it groups the objects based on their similarities. In this paper, the authors propose a new algorithm for semi supervised classification namely Semi Supervised Weighted K-Means (SSWKM. In this algorithm, the authors suggest the usage of weighted Euclidean distance metric designed as per the purpose of clustering for estimating the proximity between a pair of points and used it for building semi supervised classifier. The authors propose a new approach for estimating the weights of features by appropriately adopting the results of multiple discriminant analysis. The proposed method was then tested on benchmark datasets from UCI repository with varied percentage of labeled examples and found to be consistent and promising.

  6. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

    Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

  7. APT-KNN:一种面向分类问题的高效缺失值填充算法%APT-KNN : AN EFFICIENT MISSING VALUE IMPUTATION METHOD ORIENTED TOWARD CLASSIFICATION ISSUE

    Institute of Scientific and Technical Information of China (English)

    徐宇明; 陈诚; 熊赟; 朱扬勇

    2011-01-01

    分类是一种常见的数据挖掘方法,而属性值缺失是分类过程中常见的一类数据质量问题,缺失值填充可以减少属性值缺失造成的分类错误.缺失值填充首先要求准确率高,在许多实际应用当中,缺失值填充还必须保证较高的计算效率.提出了一种填充缺失属性值算法APT-KNN,APT-KNN算法利用属性与属性之间的相互关系,根据与目标最相似的几个实例属性值来估计缺失值,以保证填充结果具有更高的准确性,同时设计了一种优化的AntiPole树索引结构,提高了缺失属性值的填充效率.实验表明,APT-KNN方法与现有的几种缺失属性填充方法相比,具有更高的准确率和填充效率.%Classification is one of the common data mining methods. However,one common data quality problem in classification process is attribute value missing,and missing data imputation can reduce the effect on the classification errors caused by the attribute value missing.Missing data imputation requires high accuracy first, and it shall ensure higher computation efficiency in many practical applications as well. In this paper,we present a new imputation method for missed attribute value - APT-KNN ,it makes use of the relations among the attributes and estimates the missing value according to a couple of instance attribute values which are most similar to the object,so as to guarantee higher accuracy of the imputed results. At the same time,an optimised AntiPole-Tree index structure is designed, which improves the efficiency of missed attribute values imputation. Experiments show that APT-KNN outperforms several current methods of missed attribute imputation on efficiency and accuracy.

  8. Botnet analysis using ensemble classifier

    Directory of Open Access Journals (Sweden)

    Anchit Bijalwan

    2016-09-01

    Full Text Available This paper analyses the botnet traffic using Ensemble of classifier algorithm to find out bot evidence. We used ISCX dataset for training and testing purpose. We extracted the features of both training and testing datasets. After extracting the features of this dataset, we bifurcated these features into two classes, normal traffic and botnet traffic and provide labelling. Thereafter using modern data mining tool, we have applied ensemble of classifier algorithm. Our experimental results show that the performance for finding bot evidence using ensemble of classifiers is better than single classifier. Ensemble based classifiers perform better than single classifier by either combining powers of multiple algorithms or introducing diversification to the same classifier by varying input in bot analysis. Our results are showing that by using voting method of ensemble based classifier accuracy is increased up to 96.41% from 93.37%.

  9. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  10. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  11. Multi-feature classifiers for burst detection in single EEG channels from preterm infants

    Science.gov (United States)

    Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

    2017-08-01

    Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

  12. Trade Supervision Adjustment in 2012

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    China revised its supervision policies on import and export trades again, with 13 notices approved by related government ministries and administrations as well as 15 catalogues of import and export licenses being involved.

  13. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... investigates learning opportunities in supervision with multiple supervisors. This was investigated through observations and recording of supervision, and subsequent analysis of transcripts. The analyses used different perspectives on learning; learning as participation, positioning theory and variation theory....... The research illuminates how learning opportunities are created in the interaction through the scientific discussions. It also shows how multiple supervisors can contribute to supervision by providing new perspectives and opinions that have a potential for creating new understandings. The combination...

  14. Supervised Sequence Labelling with Recurrent Neural Networks

    CERN Document Server

    Graves, Alex

    2012-01-01

    Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.    The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional...

  15. PERFORMANCE EVALUATION OF VARIOUS STATISTICAL CLASSIFIERS IN DETECTING THE DISEASED CITRUS LEAVES

    Directory of Open Access Journals (Sweden)

    SUDHEER REDDY BANDI

    2013-02-01

    Full Text Available Citrus fruits are in lofty obligation because the humans consume them daily. This research aims to amend citrus production, which knows a low upshot bourgeois on the production and complex during measurements. Nowadays citrus plants grappling some traits/diseases. Harm of the insect is one of the major trait/disease. Insecticides are not ever evidenced effectual because insecticides may be toxic to some gracious of birds. Farmers get outstanding difficulties in detecting the diseases ended open eye and also it is quite expensive.Machine vision and Image processing techniques helps in sleuthing the disease mark in citrus leaves and sound job. In this search, Citrus leaves of four classes like Normal, Greasy spot, Melanose and Scab are collected and investigated using texture analysis based on the Color Co-occurrence Method (CCM to take Hue, Saturation and Intensity (HSI features. In the arrangement form, the features are categorised for all leafage conditions using k-Nearest Neighbor (kNN, Naive Bayes classifier (NBC, Linear Discriminate Analysis (LDA classifier and Random Forest Tree Algorithm classifier (RFT. The experimental results inform that proposed attack significantly supports 98.75% quality in automated detection of regular and struck leaves using texture psychotherapy based CCM method using LDA formula. Eventually all the classifiers are compared using Earphone Operative Characteristic contour and analyzed the performance of all the classifiers.

  16. 20 CFR 656.21 - Supervised recruitment.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 656.21 Section 656.21... Supervised recruitment. (a) Supervised recruitment. Where the Certifying Officer determines it appropriate, post-filing supervised recruitment may be required of the employer for the pending application or...

  17. Educational Supervision Appropriate for Psychiatry Trainee's Needs

    Science.gov (United States)

    Rele, Kiran; Tarrant, C. Jane

    2010-01-01

    Objective: The authors studied the regularity and content of supervision sessions in one of the U.K. postgraduate psychiatric training schemes (Mid-Trent). Methods: A questionnaire sent to psychiatry trainees assessed the timing and duration of supervision, content and protection of supervision time, and overall quality of supervision. The authors…

  18. 32 CFR 727.11 - Supervision.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 5 2010-07-01 2010-07-01 false Supervision. 727.11 Section 727.11 National... Supervision. The Judge Advocate General will exercise supervision over all legal assistance activities in the Department of the Navy. Subject to the supervision of the Judge Advocate General, officers in charge of...

  19. The measurement of KNN, KLL in p¯d→n¯X and p¯9Be→n¯X at 800 MeV

    Science.gov (United States)

    Riley, P. J.; Hollas, C. L.; Newsom, C. R.; Ransome, R. D.; Bonner, B. E.; Simmons, J. E.; Bhatia, T. S.; Glass, G.; Hiebert, J. C.; Northcliffe, L. C.; Tippens, W. B.

    1981-03-01

    The spin transfer parameters, KNN and KLL have been measured in pd→nX and p9Be→nX at 0° and 800 MeV. The rather large values of KLL demonstrate that this transfer mechanism will provide a useful source of polarized neutrons at LAMPF energies.

  20. Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm

    Directory of Open Access Journals (Sweden)

    Joon Heo

    2009-06-01

    Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

  1. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers.

    Science.gov (United States)

    Chou, Kuo-Chen; Shen, Hong-Bin

    2006-08-01

    Facing the explosion of newly generated protein sequences in the post genomic era, we are challenged to develop an automated method for fast and reliably annotating their subcellular locations. Knowledge of subcellular locations of proteins can provide useful hints for revealing their functions and understanding how they interact with each other in cellular networking. Unfortunately, it is both expensive and time-consuming to determine the localization of an uncharacterized protein in a living cell purely based on experiments. To tackle the challenge, a novel hybridization classifier was developed by fusing many basic individual classifiers through a voting system. The "engine" of these basic classifiers was operated by the OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) rule. As a demonstration, predictions were performed with the fusion classifier for proteins among the following 16 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cyanelle, (5) cytoplasm, (6) cytoskeleton, (7) endoplasmic reticulum, (8) extracell, (9) Golgi apparatus, (10) lysosome, (11) mitochondria, (12) nucleus, (13) peroxisome, (14) plasma membrane, (15) plastid, and (16) vacuole. To get rid of redundancy and homology bias, none of the proteins investigated here had >/=25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the jack-knife cross-validation test and independent dataset test were 81.6% and 83.7%, respectively, which were 46 approximately 63% higher than those performed by the other existing methods on the same benchmark datasets. Also, it is clearly elucidated that the overwhelmingly high success rates obtained by the fusion classifier is by no means a trivial utilization of the GO annotations as prone to be misinterpreted because there is a huge number of proteins with given accession numbers and the corresponding GO numbers, but their subcellular locations are still unknown, and that the

  2. Multivoiced Supervision of Master's Students: A Case Study of Alternative Supervision Practices in Higher Education

    Science.gov (United States)

    Dysthe, Olga; Samara, Akylina; Westrheim, Kariane

    2006-01-01

    This article describes and analyzes an alternative supervision model at the Master of Education Programme at the University of Bergen aimed at improving research supervision. A three-pronged approach was introduced, combining supervision groups, student colloquia and individual supervision. The supervision groups consisted of two supervisors and…

  3. Clinical supervision in a community setting.

    Science.gov (United States)

    Evans, Carol; Marcroft, Emma

    Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

  4. Clinical supervision: the state of the art.

    Science.gov (United States)

    Falender, Carol A; Shafranske, Edward P

    2014-11-01

    Since the recognition of clinical supervision as a distinct professional competence and a core competence, attention has turned to ensuring supervisor competence and effective supervision practice. In this article, we highlight recent developments and the state of the art in supervision, with particular emphasis on the competency-based approach. We present effective clinical supervision strategies, providing an integrated snapshot of the current status. We close with consideration of current training practices in supervision and challenges.

  5. Self-supervised Traversability Assessment in Field Environments with Lidar and Camera

    DEFF Research Database (Denmark)

    Hansen, Mikkel Kragh; Underwood, James; Karstoft, Henrik

    obstacles and threats across a variety of environmental conditions. In this paper, a self-supervised framework is proposed, combining laser range sensing from a lidar with images from a monocular camera to reliably assess terrain traversability/navigability. Methods The method uses a near-to-far approach......, where accurate near-distance classifications using lidar data are used to supervise a far-distance classifier of camera data. A simple geometric classifier detects flat, traversable ground patches using the lidar data and continuously feeds these patches as training data to a visual classifier. Finally...... to continuously adjust for environmental changes in illumination and appearance, thereby outperforming an equivalent offline-trained algorithm. Conclusion The proposed self-supervised framework successfully combines lidar and camera sensing in a near-to-far setting for assessing terrain traversability. Our...

  6. A Topic Model Approach to Representing and Classifying Football Plays

    KAUST Repository

    Varadarajan, Jagannadan

    2013-09-09

    We address the problem of modeling and classifying American Football offense teams’ plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more ef- ficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximiza- tion of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

  7. Defending Malicious Script Attacks Using Machine Learning Classifiers

    Directory of Open Access Journals (Sweden)

    Nayeem Khan

    2017-01-01

    Full Text Available The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

  8. Automatically Classifying the Role of Citations in Biomedical Articles

    Science.gov (United States)

    Agarwal, Shashank; Choubey, Lisha; Yu, Hong

    2010-01-01

    Citations are widely used in scientific literature. The traditional model of referencing considers all citations to be the same; however, semantically, citations play different roles. By studying the context in which citations appear, it is possible to determine the role that they play. Here, we report on the development of an eight-category classification scheme, annotation using that scheme, and development and evaluation of supervised machine-learning classifiers using the annotated data. We annotated 1,710 sentences using the annotation schema and our trained classifier obtained an average F1-score of 76.5%. The classifier is available for free as a Java API from http://citation.askhermes.org. PMID:21346931

  9. Performance of some supervised and unsupervised multivariate techniques for grouping authentic and unauthentic Viagra and Cialis

    Directory of Open Access Journals (Sweden)

    Michel J. Anzanello

    2014-09-01

    Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.

  10. Emergent behaviors of classifier systems

    Energy Technology Data Exchange (ETDEWEB)

    Forrest, S.; Miller, J.H.

    1989-01-01

    This paper discusses some examples of emergent behavior in classifier systems, describes some recently developed methods for studying them based on dynamical systems theory, and presents some initial results produced by the methodology. The goal of this work is to find techniques for noticing when interesting emergent behaviors of classifier systems emerge, to study how such behaviors might emerge over time, and make suggestions for designing classifier systems that exhibit preferred behaviors. 20 refs., 1 fig.

  11. Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

    Science.gov (United States)

    Yaghouby, Farid; Sunderam, Sridhar

    2015-04-01

    The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations.

  12. Application of SVM classifier in thermographic image classification for early detection of breast cancer

    Science.gov (United States)

    Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

    2016-09-01

    This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

  13. k子凸包分类方法%A k Sub-Convex-Hull Classifier

    Institute of Scientific and Technical Information of China (English)

    牟廉明

    2011-01-01

    The k-local hyperplane distance nearest neighbor classifier based convex-hull(CKNN) corrects the decision boundary of k-NN when the amount of the training data is small,thus it can improve the performance from k-NN. Since CKNN queries k nearest neighbors of a test instance from each of the classes, the performance of CKNN is sensitive to the noises and the number of classes. Moreover, when data is distributed as that one class "surrounds" the other,the hyperplane distance of the convex-hull of the "outsider" class to any "insider" instance is be zero,which makes the classes undistinguishable and thus leads to classification errors. In this paper, we propose a k sub-convex-hull classifier to address these problems by integrating convex-hull technology into k nearest neighbor classifier. After finding the k nearest neighbors of a test instance,the k sub-convex-hull classifier assigns its label by the distance of the test instance to some corresponding sub-convex-hull. The experimental results show that our k sub-convex-hull classifier is significantly superior to some state-of-the-art nearest neighbor classifiers.%基于凸包的k局部超平面距离分类方法,通过改进k近邻算法在处理小样本问题时的决策边界而显著提高分类性能.但是,该方法对噪声和类的数目敏感,并且在一类样本“包围”另一类样本时,由于外围类凸包与内部样本的距离为零而导致分类错误.针对上述问题,提出了k子凸包分类方法,该方法融合了k近邻分类和凸包技术的优点,首先寻找测试样本的k近邻,然后在该邻域中计算测试样本到相应类的子凸包的距离,并根据距离大小来确定该测试样本的类别,有效克服了k局部超平面距离分类存在的不足.大量实验表明,文章提出的k子凸包分类方法在分类性能上具有显著的优势.

  14. Challenges for Better thesis supervision.

    Science.gov (United States)

    Ghadirian, Laleh; Sayarifard, Azadeh; Majdzadeh, Reza; Rajabi, Fatemeh; Yunesian, Masoud

    2014-01-01

    Conduction of thesis by the students is one of their major academic activities. Thesis quality and acquired experiences are highly dependent on the supervision. Our study is aimed at identifing the challenges in thesis supervision from both students and faculty members point of view. This study was conducted using individual in-depth interviews and Focus Group Discussions (FGD). The participants were 43 students and faculty members selected by purposive sampling. It was carried out in Tehran University of Medical Sciences in 2012. Data analysis was done concurrently with data gathering using content analysis method. Our data analysis resulted in 162 codes, 17 subcategories and 4 major categories, "supervisory knowledge and skills", "atmosphere", "bylaws and regulations relating to supervision" and "monitoring and evaluation". This study showed that more attention and planning in needed for modifying related rules and regulations, qualitative and quantitative improvement in mentorship training, research atmosphere improvement and effective monitoring and evaluation in supervisory area.

  15. Using Both HSV Color and Texture Features to Classify Archaeological Fragments

    Directory of Open Access Journals (Sweden)

    Nada A. Rasheed

    2015-08-01

    Full Text Available Normally, the artifacts are found in a fractured state and mixed randomly and the process of manual classification may requires a great deal of time and tedious work. Therefore, classifying these fragments is a challenging task, especially if the archaeological object consists of thousands of fragments. Hence, it is important to come up with a solution for the classification of the archaeological fragments accurately into groups and reassembling each group to original form by using computer techniques. In this study we interested to find the solve to this problem depending on color and texture features, to accomplish that the algorithm begins by partition the image into six sub-blocks. Furthermore, extract HSV color space feature from each block, then this feature represent into a cumulative histogram, as a result we obtain six vectors for each image. Regard to extract the texture feature for each sub-block it will be used the Gray Level Co-occurrence Matrix (GLCM that include Energy, Contrast, Correlation and Homogeneity. At the final stage, based on k-Nearest Neighbors algorithm (KNN classifies the color and texture features, this method able to classify the fragments with a high accuracy. The algorithm was tested on several images of pottery fragments and yield results with accuracy as high as 86.51% of original grouped cases correctly classified.

  16. A multiple classifier system based on Ant-Colony Optimization for Hyperspectral image classification

    Science.gov (United States)

    Tang, Ke; Xie, Li; Li, Guangyao

    2017-01-01

    Hyperspectral images which hold a large quantity of land information enables image classification. Traditional classification methods usually works on multispectral images. However, the high dimensionality in feature space influences the accuracy while using these classification algorithms, such as statistical classifiers or decision trees. This paper proposes a multiple classifier system (MCS) based on ant colony optimization (ACO) algorithm to improve the classification ability. ACO method has been implemented on multispectral images in researches, but seldom to hyperspectral images. In order to overcome the limitation of ACO method on dealing with high dimensionality, MCS is introduced to combine the outputs of each single ACO classifier based on the credibility of rules. Mutual information is applied to discretizing features from the data set and provides the criterion of band selection and band grouping algorithms. The performance of the proposed method is validated with ROSIS Pavia data set, and compared to k-nearest neighbour (KNN) algorithm. Experimental results prove that the proposed method is feasible to classify hyperspectral images.

  17. Feature Selection and Effective Classifiers.

    Science.gov (United States)

    Deogun, Jitender S.; Choubey, Suresh K.; Raghavan, Vijay V.; Sever, Hayri

    1998-01-01

    Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…

  18. Automatic classification of Deep Web sources based on KNN algorithm%基于K-近邻算法的Deep Web数据源的自动分类

    Institute of Scientific and Technical Information of China (English)

    张智; 顾韵华

    2011-01-01

    To meet the need of Deep Web query, an algorithm for classification of Deep Web sources based on KNN is put forward. The algorithm extracts the form features from Web pages, and makes the form features vector normal. Then the algorithm classifies Deep Web pages by computing distance. The experimental results show that the algorithm has improved in precision and recall.%针对Deep Web的查询需求,提出了一种基于K-近邻算法的Deep Web数据源的自动分类方法.该算法在对Deep Web网页进行表单特征提取及规范化的基础上,基于距离对Deep Web网页所属的目标主题进行判定.实验结果表明:基于K-近邻分类算法可以较有效地进行DeepWeb数据源的自动分类,并得到较高的查全率和查准率.

  19. Development of a combined GIS, neural network and Bayesian classifier methodology for classifying remotely sensed data

    Science.gov (United States)

    Schneider, Claudio Albert

    This research is aimed at the solution of two common but still largely unsolved problems in the classification of remotely sensed data: (1) Classification accuracy of remotely sensed data decreases significantly in mountainous terrain, where topography strongly influences the spectral response of the features on the ground; and (2) when attempting to obtain more detailed classifications, e.g. forest cover types or species, rather than just broad categories of forest such as coniferous or deciduous, the accuracy of the classification generally decreases significantly. The main objective of the study was to develop a widely applicable and efficient classification procedure for mapping forest and other cover types in mountainous terrain, using an integrated GIS/neural network/Bayesian classification approach. The performance of this new technique was compared to a standard supervised Maximum Likelihood classification technique, a "conventional" Bayesian/Maximum Likelihood classification, and to a "conventional" neural network classifier. Results indicate a considerable improvement of the new technique over the standard Maximum Likelihood classification technique, as well as a better accuracy than the "conventional" Bayesian/Maximum Likelihood classifier (13.08 percent improvement in overall accuracy), but the "conventional" neural network classifiers outperformed all the techniques compared in this study, with an overall accuracy improvement of 15.94 percent as compared to the standard Maximum Likelihood classifier (from 46.77 percent to 62.71 percent). However, the overall accuracies of all the classification techniques compared in this study were relative low. It is believed that this was caused by problems related to the inadequacy of the reference data. On the other hand, the results also indicate the need to develop a different sampling design to more effectively cover the variability across all the parameters needed by the neural network classification technique

  20. Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

    Science.gov (United States)

    Siuly; Yin, Xiaoxia; Hadjiloucas, Sillas; Zhang, Yanchun

    2016-04-01

    This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive. The problem is addressed using two-dimensional (2-D) cross-correlations between background and sample interferograms, these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers. A cross-validation procedure is adopted to assess the performance of the classifiers. Firstly the measurements related to samples that had thicknesses of 2mm were classified, then samples at thicknesses of 4mm, and after that 3mm were classified and the success rate and consistency of each classifier was recorded. In addition, mixtures having thicknesses of 2 and 4mm as well as mixtures of 2, 3 and 4mm were presented simultaneously to all classifiers. This approach provided further cross-validation of the classification consistency of each algorithm. The results confirm the superiority in classification accuracy and robustness of the MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) algorithms which consistently outperformed the SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) classifiers for the same number of feature vectors across all studies

  1. Ethics in Rehabilitation Counselor Supervision.

    Science.gov (United States)

    Blackwell, Terry L.; Strohmer, Douglas C.; Belcas, Eva M.; Burton, Kathryn A.

    2002-01-01

    Article is an exploration of some of the ethical issues facing rehabilitation counselors who provide clinical supervision. Ethical issues related to competence, evaluation and due process, dual relationships, confidentiality, and informed consent are discussed. (Contains 28references, 2 tables, and 1 appendix.) (Author)

  2. Sampling Based Average Classifier Fusion

    Directory of Open Access Journals (Sweden)

    Jian Hou

    2014-01-01

    fusion algorithms have been proposed in literature, average fusion is almost always selected as the baseline for comparison. Little is done on exploring the potential of average fusion and proposing a better baseline. In this paper we empirically investigate the behavior of soft labels and classifiers in average fusion. As a result, we find that; by proper sampling of soft labels and classifiers, the average fusion performance can be evidently improved. This result presents sampling based average fusion as a better baseline; that is, a newly proposed classifier fusion algorithm should at least perform better than this baseline in order to demonstrate its effectiveness.

  3. Classified

    CERN Multimedia

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  4. The Application of KNN-FCM Clustering Algorithm in Text Filtering of Chinese Search Engine%KNN-FCM聚类算法在中文搜索引擎文本过滤中的应用

    Institute of Scientific and Technical Information of China (English)

    张俊丽; 张帆

    2007-01-01

    目前,大多数搜索引擎都是用相关度或page-rank或HITS(Hyperlink-Induced Topic Search)算法对匹配的结果进行排序,然后以列表的方式呈现给用户.事实表明:其索引质量不高,对所收集的信息缺乏有效的分类处理,用户面对成千上万的搜索结果无法--查看,而真正符合需要的搜索结果常常因为排在后面而被漏检,返回的结果只有极少部分得到了用户的有效利用.文章提出运用基于K近邻的模糊C均值算法(以下简称KNN-FCM)对搜索引擎的初始结果进行自动聚类,系统再针对用户作出的适时反馈进行相应的输出调整,从而方便用户查找信息.

  5. Combining classifiers for robust PICO element detection

    Directory of Open Access Journals (Sweden)

    Grad Roland

    2010-05-01

    Full Text Available Abstract Background Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents. Methods In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element. Results Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an f-measure score of 86.3% for P, 67% for I and 56.6% for O. Conclusions Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.

  6. Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

    Institute of Scientific and Technical Information of China (English)

    Wissal Drira; Faouzi Ghorbel

    2014-01-01

    This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

  7. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.;

    2015-01-01

    well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data...

  8. An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

    Directory of Open Access Journals (Sweden)

    Deborah Galpert

    2015-01-01

    Full Text Available Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

  9. Optimally Training a Cascade Classifier

    CERN Document Server

    Shen, Chunhua; Hengel, Anton van den

    2010-01-01

    Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of \\cite{wu2005linear}. We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed bo...

  10. Combining different types of classifiers

    OpenAIRE

    Gatnar, Eugeniusz

    2008-01-01

    Model fusion has proved to be a very successful strategy for obtaining accurate models in classification and regression. The key issue, however, is the diversity of the component classifiers because classification error of an ensemble depends on the correlation between its members. The majority of existing ensemble methods combine the same type of models, e.g. trees. In order to promote the diversity of the ensemble members, we propose to aggregate classifiers of different t...

  11. 20 CFR 655.30 - Supervised recruitment.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 655.30 Section 655.30... Workers) § 655.30 Supervised recruitment. (a) Supervised recruitment. Where an employer is found to have... failed to adequately conduct recruitment activities or failed in any obligation of this part, the CO may...

  12. 40 CFR 35.935-8 - Supervision.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Supervision. 35.935-8 Section 35.935-8... ASSISTANCE Grants for Construction of Treatment Works-Clean Water Act § 35.935-8 Supervision. In the case of... supervision and inspection of the project to ensure that the construction conforms with the approved plans...

  13. Supervision Experiences of New Professional School Counselors

    Science.gov (United States)

    Bultsma, Shawn A.

    2012-01-01

    This qualitative study examined the supervision experiences of 11 new professional school counselors. They reported that their supervision experiences were most often administrative in nature; reports of clinical and developmental supervision were limited to participants whose supervisors were licensed as professional counselors. In addition,…

  14. 10 CFR 35.27 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 1 2010-01-01 2010-01-01 false Supervision. 35.27 Section 35.27 Energy NUCLEAR REGULATORY COMMISSION MEDICAL USE OF BYPRODUCT MATERIAL General Administrative Requirements § 35.27 Supervision. (a) A... under the supervision of an authorized user, as allowed by § 35.11(b)(1), shall— (1) In addition to...

  15. 28 CFR 2.91 - Supervision responsibility.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision responsibility. 2.91 Section 2.91 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF....91 Supervision responsibility. (a) Pursuant to D.C. Code 24-133(c), the District of Columbia...

  16. 75 FR 59799 - Office of Thrift Supervision

    Science.gov (United States)

    2010-09-28

    ... Office of Thrift Supervision Purchase of Branch Office(s) and/or Transfer of Assets/Liabilities AGENCY: Office of Thrift Supervision (OTS), Treasury. ACTION: Notice and request for comment. SUMMARY: The... Supervision within the Department of the Treasury will submit the proposed information collection...

  17. 7 CFR 70.12 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 3 2010-01-01 2010-01-01 false Supervision. 70.12 Section 70.12 Agriculture... PRODUCTS AND RABBIT PRODUCTS Grading of Poultry Products and Rabbit Products General § 70.12 Supervision. All grading service shall be subject to supervision at all times by the responsible State...

  18. 7 CFR 550.33 - Administrative supervision.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Administrative supervision. 550.33 Section 550.33... Agreements Program Management § 550.33 Administrative supervision. REE employees are prohibited from engaging... management issues. The cooperator is solely responsible for the administrative supervision of its employees....

  19. 27 CFR 46.79 - Supervision.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 2 2010-04-01 2010-04-01 false Supervision. 46.79 Section... § 46.79 Supervision. Before payment is made under this subpart in respect of the tax, or tax and duty... under the supervision of an appropriate TTB officer who will be assigned for that purpose by...

  20. 24 CFR 200.105 - Mortgagor supervision.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 2 2010-04-01 2010-04-01 false Mortgagor supervision. 200.105... supervision. (a) As long as the Commissioner is the insurer or holder of the mortgage, the Commissioner shall... Regulatory Agreement or other instrument granting the Commissioner supervision of the mortgagor....

  1. The Learning Alliance: Ethics in Doctoral Supervision

    Science.gov (United States)

    Halse, Christine; Bansel, Peter

    2012-01-01

    This paper is concerned with the ethics of relationships in doctoral supervision. We give an overview of four paradigms of doctoral supervision that have endured over the past 25 years and elucidate some of their strengths and limitations, contextualise them historically and consider their implications for doctoral supervision in the contemporary…

  2. 48 CFR 836.572 - Government supervision.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Government supervision. 836.572 Section 836.572 Federal Acquisition Regulations System DEPARTMENT OF VETERANS AFFAIRS SPECIAL... supervision. The contracting officer shall insert the clause at 852.236-78, Government supervision,...

  3. 7 CFR 56.6 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 3 2010-01-01 2010-01-01 false Supervision. 56.6 Section 56.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards, Inspections... Grading of Shell Eggs General § 56.6 Supervision. All grading service shall be subject to supervision...

  4. 32 CFR 631.3 - Supervision.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 4 2010-07-01 2010-07-01 true Supervision. 631.3 Section 631.3 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND CRIMINAL....3 Supervision. The following will develop and have staff supervision over AFDCB and...

  5. 9 CFR 354.13 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Supervision. 354.13 Section 354.13... CERTIFICATION VOLUNTARY INSPECTION OF RABBITS AND EDIBLE PRODUCTS THEREOF Basis of Service § 354.13 Supervision. All inspection service shall be subject to supervision at all times by the station supervisor,...

  6. Skærpet bevidsthed om supervision

    DEFF Research Database (Denmark)

    Pedersen, Inge Nygaard

    2002-01-01

    This article presents a historical survey of the initiatives which have taken place in european music therapy towards developing a deeper consciousness about supervision. Supervision as a disciplin in music therapy training, as a maintenance of music therapy profession and as a postgraduate...... training for examined music therapists. Definitions are presented and methods developed by working groups in european music therapy supervision are presented....

  7. Multicultural Supervision: What Difference Does Difference Make?

    Science.gov (United States)

    Eklund, Katie; Aros-O'Malley, Megan; Murrieta, Imelda

    2014-01-01

    Multicultural sensitivity and competency represent critical components to contemporary practice and supervision in school psychology. Internship and supervision experiences are a capstone experience for many new school psychologists; however, few receive formal training and supervision in multicultural competencies. As an increased number of…

  8. Optimal weighted nearest neighbour classifiers

    CERN Document Server

    Samworth, Richard J

    2011-01-01

    We derive an asymptotic expansion for the excess risk (regret) of a weighted nearest-neighbour classifier. This allows us to find the asymptotically optimal vector of non-negative weights, which has a rather simple form. We show that the ratio of the regret of this classifier to that of an unweighted $k$-nearest neighbour classifier depends asymptotically only on the dimension $d$ of the feature vectors, and not on the underlying population densities. The improvement is greatest when $d=4$, but thereafter decreases as $d \\rightarrow \\infty$. The popular bagged nearest neighbour classifier can also be regarded as a weighted nearest neighbour classifier, and we show that its corresponding weights are somewhat suboptimal when $d$ is small (in particular, worse than those of the unweighted $k$-nearest neighbour classifier when $d=1$), but are close to optimal when $d$ is large. Finally, we argue that improvements in the rate of convergence are possible under stronger smoothness assumptions, provided we allow nega...

  9. A National Survey of School Counselor Supervision Practices: Administrative, Clinical, Peer, and Technology Mediated Supervision

    Science.gov (United States)

    Perera-Diltz, Dilani M.; Mason, Kimberly L.

    2012-01-01

    Supervision is vital for personal and professional development of counselors. Practicing school counselors (n = 1557) across the nation were surveyed to explore current supervision practices. Results indicated that 41.1% of school counselors provide supervision. Although 89% receive some type of supervision, only 10.3% of school counselors receive…

  10. 3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM

    Directory of Open Access Journals (Sweden)

    P. S. Hiremath

    2014-06-01

    Full Text Available Biometrics (or biometric authentication refers to the identification of humans by their characteristics or traits. Bio-metrics is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Three dimensional (3D human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D face recognition. In the previous research works, there are several methods for face recognition using range images that are limited to the data acquisition and pre-processing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, Principal Component Analysis (PCA and Linear Discriminant Analysis (LDA. The Radon transform (RT is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face databases. The experimental results are shown that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT+PCA. It is observed that 40 Eigen faces of PCA and 5 LDA components lead to an average recognition rate of 99.20% using SVM classifier.

  11. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  12. A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

    Science.gov (United States)

    2016-07-01

    closed-form expression for the class of each node is derived. Moreover, the authors of [50] describe a semi-supervised method for classifying data using...manifold smoothing and image denoising. In addition to image processing, methods in- volving spectral graph theory [17,56], based on a graphical setting...pagerank and Section 3 presents a model using heat kernel pagerank directly as a classifier . Section 4 formulates the new algorithm as well as provides

  13. Remarks on Vertex-Distinguishing IE-Total Coloring of Complete Bipartite Graphs K4,n and Kn,n

    Institute of Scientific and Technical Information of China (English)

    Xiang'en CHEN; Xiaoqing XIN; Wenyu HE

    2012-01-01

    Let G be a simple graph.An IE-total coloring f of G refers to a coloring of the vertices and edges of G so that no two adjacent vertices receive the same color.Let C(u) be the set of colors of vertex u and edges incident to u under f.For an IE-total coloring f of G using k colors,if C(u) ≠ C(v) for any two different vertices u and v of V(G),then f is called a k-vertex-distinguishing IE-total-coloring of G,or a k-VDIET coloring of G for short.The minimum number of colors required for a VDIET coloring of G is denoted by xievt(G),and it is called the VDIET chromatic number of G.We will give VDIET chromatic numbers for complete bipartite graph K4,n (n ≥ 4),Kn,n (5 ≤ n ≤ 21) in this article.

  14. IMAGE LABELING FOR LIDAR INTENSITY IMAGE USING K-NN OF FEATURE OBTAINED BY CONVOLUTIONAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Umemura

    2016-06-01

    Full Text Available We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS using K-Nearest Neighbor (KNN of feature obtained by Convolutional Neural Network (CNN. Image labeling assigns labels (e.g., road, cross-walk and road shoulder to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images and test sets (8 images. We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.

  15. Image Labeling for LIDAR Intensity Image Using K-Nn of Feature Obtained by Convolutional Neural Network

    Science.gov (United States)

    Umemura, Masaki; Hotta, Kazuhiro; Nonaka, Hideki; Oda, Kazuo

    2016-06-01

    We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image labeling assigns labels (e.g., road, cross-walk and road shoulder) to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet) pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.

  16. Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

    Directory of Open Access Journals (Sweden)

    Muhammad Bilal

    2016-07-01

    Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.

  17. Supervision Experiences of Professional Counselors Providing Crisis Counseling

    Science.gov (United States)

    Dupre, Madeleine; Echterling, Lennis G.; Meixner, Cara; Anderson, Robin; Kielty, Michele

    2014-01-01

    In this phenomenological study, the authors explored supervision experiences of 13 licensed professional counselors in situations requiring crisis counseling. Five themes concerning crisis and supervision were identified from individual interviews. Findings support intensive, immediate crisis supervision and postlicensure clinical supervision.

  18. Hybrid classifiers methods of data, knowledge, and classifier combination

    CERN Document Server

    Wozniak, Michal

    2014-01-01

    This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

  19. Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

    Science.gov (United States)

    Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

    2017-03-01

    Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

  20. Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

    Energy Technology Data Exchange (ETDEWEB)

    Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

    2011-04-15

    An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

  1. Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

    Science.gov (United States)

    Provost, F.; Hibert, C.; Malet, J.-P.

    2017-01-01

    The deformation of slow-moving landslides developed in clays induces endogenous seismicity of mostly low-magnitude events (MLAlps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.

  2. [Software version and medical device software supervision].

    Science.gov (United States)

    Peng, Liang; Liu, Xiaoyan

    2015-01-01

    The importance of software version in the medical device software supervision does not cause enough attention at present. First of all, the effect of software version in the medical device software supervision is discussed, and then the necessity of software version in the medical device software supervision is analyzed based on the discussion of the misunderstanding of software version. Finally the concrete suggestions on software version naming rules, software version supervision for the software in medical devices, and software version supervision scheme are proposed.

  3. Towards designing an email classification system using multi-view based semi-supervised learning

    NARCIS (Netherlands)

    Li, Wenjuan; Meng, Weizhi; Tan, Zhiyuan; Xiang, Yang

    2014-01-01

    The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive t

  4. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.;

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

  5. Predicting incomplete gene microarray data with the use of supervised learning algorithms

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

    Full Text Available of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain...

  6. Social media research: The application of supervised machine learning in organizational communication research

    NARCIS (Netherlands)

    van Zoonen, W.; van der Meer, T.G.L.A.

    2016-01-01

    Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary dat

  7. Clinical supervision training across contexts.

    Science.gov (United States)

    Tai, Joanna; Bearman, Margaret; Edouard, Vicki; Kent, Fiona; Nestel, Debra; Molloy, Elizabeth

    2016-08-01

    Clinicians require specific skills to teach or supervise students in the workplace; however, there are barriers to accessing faculty member development, such as time, cost and suitability. The Clinical Supervision Support Across Contexts (ClinSSAC) programme was designed to provide accessible interprofessional educator training to clinical supervisors across a wide range of clinical settings. In Australia there are increasing numbers of health care students, creating pressure on existing placements. Students are now increasingly learning in community settings, where clinicians have traditionally had less access to faculty member development. An interprofessional team collaborated in the development and implementation of ClinSSAC. A total of 978 clinicians participated in a face-to-face, interactive, introductory module to clinical supervision; 672 people accessed the equivalent online core module, with 23 per cent completing all activities. Additional profession-and discipline-specific modules were also developed. Formal project evaluation found that most participants rated the workshops as helpful or very helpful for their roles as clinical supervisors. Interdisciplinary learning from the workshops was reported to enable cross-discipline supervision. Large participant numbers and favourable ratings indicate a continuing need for basic training in education. Key factors to workshop success included expert facilitators, the interprofessional context and interactive model. The online modules were an important adjunct, and provided context-specific resources, but the low online completion rate suggests protected face-to-face time for faculty member development is still required. Programmes such as ClinSSAC have the capacity to promote interprofessional education and practice. There are barriers to accessing faculty member development, such as time, cost and suitability. © 2015 John Wiley & Sons Ltd.

  8. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... of an introduction course for new doctoral students. This study showed how the course provides an effective way build supervisee agency and strengthening supervisory relationships through clarification and alignment of expectations and sharing goals about doctoral studies. From the other angle the research...

  9. Clustering of Rotavirus Based on KNN-kernel Function%基于KNN核函数聚类的轮状病毒统计分析

    Institute of Scientific and Technical Information of China (English)

    许华萍

    2015-01-01

    Objective] To discuss application of KNN-kernel clustering methods for diarrhea patients serum immune indexes detection data classification and diagnosis of applicability and clinical significance. [Methods] To reveal the applicability and clinical signnificance of KNN-kernel function clustering method in the diagnosis of serun immune index. In this research, the KNNCLUST algorithm is used to program the serum immune index data of 74 patients with diarrhea by Matlab software. [Results] 74 patients were divided into 5 categories by cluster analysis. The patients with diarrhea were divided into rotavirus negative and positive class, and the patients were further subdivided, especially the three early rotavirus tests were negative but later confirmed positive and were clustered into one group. [Conclusions] This can be seen that the KNN-kernel clustering method is helpful for early screening of rotavirus infection, practical clinical significance on the early treatment of disease.%[目的]探讨K最近邻(K-Nearest Neighbors, KNN)核函数聚类方法在腹泻患者血清免疫指标分类诊断中的适用性和临床意义。[方法]利用KNNCLUST算法的原理和步骤用Matlab软件进行编程,对74例腹泻患者的血清免疫指标数据进行聚类分析,揭示KNN-核函数聚类方法在腹泻患者血清免疫指标分类诊断中的适用性和临床意义。[结果]74例患者经聚类分析分成了5类。该分类不仅把腹泻患者分成轮状病毒阴性和阳性两类,而且把患者进一步进行细分,尤其是把3个初期轮状病毒检测阴性但后期证实是阳性的患者聚成一类。[结论]应用基于KNN-核函数的非参数聚类方法,有助于筛选前期轮状病毒感染者,对疾病的早期诊断治疗具有一定临床意义。

  10. Data driven information system for supervision of judicial open

    Directory of Open Access Journals (Sweden)

    Ming LI

    2016-08-01

    Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

  11. Nursing supervision for care comprehensiveness.

    Science.gov (United States)

    Chaves, Lucieli Dias Pedreschi; Mininel, Vivian Aline; Silva, Jaqueline Alcântara Marcelino da; Alves, Larissa Roberta; Silva, Maria Ferreira da; Camelo, Silvia Helena Henriques

    2017-01-01

    To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks. refletir a supervisão de enfermagem como instrumento gerencial do enfermeiro para integralidade do cuidado, considerando suas potencialidades e limitações no cenário atual. estudo reflexivo baseado na formulação discursiva sobre a supervisão de enfermagem, apresentando conceitos e enfoques teóricos e/ou práticos. limitações no exercício da supervisão estão relacionadas à organização dos serviços de saúde embasada no modelo funcional e clínico de atenção, assim como possíveis lacunas no processo de formação do enfermeiro e sobrecarga de trabalho. Quanto às potencialidades, destaca-se a supervisão como instrumento de articulação de ações assistenciais e gerenciais, que pode favorecer integralidade da atenção, estimular atitudes de cooperação e colaboração em equipe, além da corresponsabilização e promoção da educação no trabalho. supervisão de enfermagem pode contribuir para fortalecimento da integralidade do cuidado, pressupondo reflexão cont

  12. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  13. Maximum margin Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael; Tschiatschek, Sebastian

    2012-03-01

    We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  14. Classifying Cereal Data (Earlier Methods)

    Science.gov (United States)

    The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

  15. An ensemble self-training protein interaction article classifier.

    Science.gov (United States)

    Chen, Yifei; Hou, Ping; Manderick, Bernard

    2014-01-01

    Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

  16. Data Stream Classification Based on the Gamma Classifier

    Directory of Open Access Journals (Sweden)

    Abril Valeria Uriarte-Arcia

    2015-01-01

    Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

  17. Multiple-instance learning as a classifier combining problem

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M. J.; Duin, Robert P. W.

    2013-01-01

    In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the ass......In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL...... with the assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification...... posteriors. Given the instance labels, the label of a bag can be obtained as a classifier combining problem. An optimal decision rule is derived that determines the threshold on the fraction of instances in a bag that is assigned to the concept class. We provide estimators for the two parameters in the model...

  18. Weighted Hybrid Decision Tree Model for Random Forest Classifier

    Science.gov (United States)

    Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.

    2016-06-01

    Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

  19. 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.

  20. Sundhedsfaglig supervision som klinisk metode

    DEFF Research Database (Denmark)

    Nordentoft, Helle Merete

    2011-01-01

    Kapitlet gennemgår og diskuterer sundhedsfaglig supervision (SFS) som metode. Formålet med kapitlet er at give læseren indsigt i og et kritisk blik på metodens muligheder og begrænsninger. Indledningsvis uddyber kapitlet den historiske baggrund for SFS og hvordan metoden udfolder sig i praksis me...... metodens fremtidige perspektiver - set i lyset af de mange nye metoder, der 'kæmper' om de samme målgrupper i vejledningslandskabet så som f.eks. coaching, debriefing og mentoring....

  1. Self-Supervised Dynamical Systems

    Science.gov (United States)

    Zak, Michail

    2003-01-01

    Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and

  2. Supervised Object Class Colour Normalisation

    DEFF Research Database (Denmark)

    Riabchenko, Ekatarina; Lankinen, Jukka; Buch, Anders Glent;

    2013-01-01

    Colour is an important cue in many applications of computer vision and image processing, but robust usage often requires estimation of the unknown illuminant colour. Usually, to obtain images invariant to the illumination conditions under which they were taken, color normalisation is used....... In this work, we develop a such colour normalisation technique, where true colours are not important per se but where examples of same classes have photometrically consistent appearance. This is achieved by supervised estimation of a class specic canonical colour space where the examples have minimal variation...

  3. Classifying self-gravitating radiations

    CERN Document Server

    Kim, Hyeong-Chan

    2016-01-01

    We study static systems of self-gravitating radiations confined in a sphere by using numerical and analytic calculations. We classify and analyze the solutions systematically. Due to the scaling symmetry, any solution can be represented as a segment of a solution curve on a plane of two-dimensional scale invariant variables. We find that a system can be conveniently parametrized by three parameters representing the solution curve, the scaling, and the system size, instead of the parameters defined at the outer boundary. The solution curves are classified to three types representing regular solutions, conically singular solutions with, and without an object which resembles an event horizon up to causal disconnectedness. For the last type, the behavior of a self-gravitating system is simple enough to allow analytic calculations.

  4. Status Quo Of Drug Supervision in China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    @@ Drugs are a special kind of commodity used to prevent,treat and diagnose diseases.Effective supervision of drugs has a great bearing on ensuring the safe use of pharmaceuticals by consumers and on safeguarding the right to lire and health of the general public.The Chinese government has always attached great importance to supervision over drug safety,and has always been commltted to the goal of strengthening such supervision and guaranteeing public drug safety.

  5. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  6. Benchmarking protein classification algorithms via supervised cross-validation.

    Science.gov (United States)

    Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

    2008-04-24

    Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and

  7. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... Classified National Security Information AGENCY: Marine Mammal Commission. ACTION: Notice. SUMMARY: This... information, as directed by Information Security Oversight Office regulations. FOR FURTHER INFORMATION CONTACT..., ``Classified National Security Information,'' and 32 CFR part 2001, ``Classified National Security......

  8. Energy-Efficient Neuromorphic Classifiers.

    Science.gov (United States)

    Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

    2016-10-01

    Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

  9. ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY

    Directory of Open Access Journals (Sweden)

    Felipe Schneider Costa

    2013-01-01

    Full Text Available The naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables, the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naïve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naïve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.

  10. An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity.

    Directory of Open Access Journals (Sweden)

    Liqi Li

    Full Text Available With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor and SVM (support vector machine algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.

  11. Supervised Discrete Hashing With Relaxation.

    Science.gov (United States)

    Gui, Jie; Liu, Tongliang; Sun, Zhenan; Tao, Dacheng; Tan, Tieniu

    2016-12-29

    Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called ''supervised discrete hashing with relaxation'' (SDHR) based on ''supervised discrete hashing'' (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.

  12. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  13. Clinical supervision by consultants in teaching hospitals.

    Science.gov (United States)

    Hore, Craig T; Lancashire, William; Fassett, Robert G

    2009-08-17

    Clinical supervision is a vital part of postgraduate medical education. Without it, trainees may not learn effectively from their experiences; this may lead to acceptance by registrars and junior doctors of lower standards of care. Currently, supervision is provided by consultants to registrars and junior doctors, and by registrars to junior doctors. Evidence suggests that the clinical supervision provided to postgraduate doctors is inadequate. Registrars and juniors doctors have the right to expect supervision in the workplace. Impediments to the provision of clinical supervision include competing demands of hospital service provision on trainees and supervisors, lack of clarity of job descriptions, private versus public commitments of supervisors and lack of interest. Supervisors should be trained in the process of supervision and provided with the time and resources to conduct it. Those being supervised should be provided with clear expectations of the process. We need to create and develop systems, environments and cultures that support high standards of conduct and effective clinical supervision. These systems must ensure the right to supervision, feedback, support, decent working conditions and respect for both trainees and their supervisors.

  14. Pixel Classification of SAR ice images using ANFIS-PSO Classifier

    Directory of Open Access Journals (Sweden)

    G. Vasumathi

    2016-12-01

    Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

  15. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor......This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable...

  16. Texture feature selection with relevance learning to classify interstitial lung disease patterns

    Science.gov (United States)

    Huber, Markus B.; Bunte, Kerstin; Nagarajan, Mahesh B.; Biehl, Michael; Ray, Lawrence A.; Wismueller, Axel

    2011-03-01

    The Generalized Matrix Learning Vector Quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography (HRCT) images. After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. Texture features were extracted from gray-level co-occurrence matrices (GLCMs), and were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. A k-nearest-neighbor (kNN) classifier and a Support Vector Machine with a radial basis function kernel (SVMrbf) were optimized in a 10-fold crossvalidation for different texture feature sets. In our experiment with real-world data, the feature sets selected by the GMLVQ approach had a significantly better classification performance compared with feature sets selected by a MI ranking.

  17. Statistical and neural classifiers in estimating rain rate from weather radar measurements

    Directory of Open Access Journals (Sweden)

    S. C. Michaelides

    2007-04-01

    Full Text Available Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

  18. 基于KD-Tree的KNN文本分类算法%KNN Algorithm for Text Classification Based on KD-Tree

    Institute of Scientific and Technical Information of China (English)

    刘忠; 刘洋; 建晓

    2012-01-01

    This paper apply KD-Tree to KNN text classification algorithm,firstly put a training text set into a KD-Tree,then search KD-Tree for the all parents nodes of the tested text node,the set including these parents text nodes is the most nearest text set,the type of the tested text is the same as the type of the most nearest text which has the most similarity with the test text,this algorithm decreases the number of the compared texts,and the time complexity is o(log2N).Experiments show that the improved KNN text classification algorithm is better than the traditional KNN text classification in classification efficiency.%本文将KD-Tree应用到KNN文本分类算法中,先对训练文本集建立一个KD-Tree,然后在KD-Tree中搜索测试文本的所有祖先节点文本,这些祖先节点文本集合就是待测文本的最邻近文本集合,与测试文本有最大相似度的祖先的文本类型就是待测试文本的类型,这种算法大大减少了参与比较的向量文本数目,时间复杂度仅为O(log2N)。实验表明,改进后的KNN文本分类算法具有比传统KNN文本分类法更高的分类效率。

  19. On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding

    DEFF Research Database (Denmark)

    Bespalov, Dmitriy; Dahl, Anders Lindbjerg; Bai, Bing

    2011-01-01

    Concept-based representation —combined with some classifier (e.g., support vector machine) or regression analysis (e.g., linear regression)—induces a popular approach among image processing community, used to infer image labels. We propose a supervised learning procedure to obtain an embedding...... to a latent concept space with the pre-defined inner product. This learning procedure uses rank minimization of the sought inner product matrix, defined in the original concept space, to find an embedding to a new low dimensional space. The empirical evidence show that the proposed supervised learning method...

  20. Diversity Competent Group Work Supervision: An Application of the Supervision of Group Work Model (SGW)

    Science.gov (United States)

    Okech, Jane E. Atieno; Rubel, Deborah

    2007-01-01

    This article emphasizes the need for concrete descriptions of supervision to promote diversity-competent group work and presents an application of the supervision of group work model (SGW) to this end. The SGW, a supervision model adapted from the discrimination model, is uniquely suited for promoting diversity competence in group work, since it…

  1. Diversity Competent Group Work Supervision: An Application of the Supervision of Group Work Model (SGW)

    Science.gov (United States)

    Okech, Jane E. Atieno; Rubel, Deborah

    2007-01-01

    This article emphasizes the need for concrete descriptions of supervision to promote diversity-competent group work and presents an application of the supervision of group work model (SGW) to this end. The SGW, a supervision model adapted from the discrimination model, is uniquely suited for promoting diversity competence in group work, since it…

  2. Implementability of Instructional Supervision as a Contemporary Educational Supervision Model in Turkish Education System

    OpenAIRE

    2012-01-01

    In this study, implementability of instructional supervision as one of contemporary educational supervision models in Turkish Education System was evaluated. Instructional supervision which aims to develop instructional processes and increase the quality of student learning based on observation of classroom activities requires collaboration among supervisors and teachers. In this literature review, significant problems have been detected due to structural organization, structural and control-...

  3. Empirical study of supervised gene screening

    Directory of Open Access Journals (Sweden)

    Ma Shuangge

    2006-12-01

    Full Text Available Abstract Background Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1 unsupervised gene screening; (2 supervised gene screening; and (3 statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. Results We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. Conclusion From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1 genes passed different supervised screenings may be considerably different; (2 concordance may vary, depending on the underlying data structure and percentage of selected genes; (3 evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4 concordance cannot be improved by supervised screening based on reproducibility.

  4. 19 CFR 146.4 - Operator responsibility and supervision.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 2 2010-04-01 2010-04-01 false Operator responsibility and supervision. 146.4... and supervision. (a) Supervision. The operator shall supervise all admissions, transfers, removals... conditions of storage in the zone as required by law and regulations. Supervision by the operator shall...

  5. Defining and Classifying Interest Groups

    DEFF Research Database (Denmark)

    Baroni, Laura; Carroll, Brendan; Chalmers, Adam;

    2014-01-01

    The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...

  6. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P

    2011-11-01

    Full Text Available -based learning algorithms. Machine Learning, 6: pp: 37-66. Amit, Y., D. Geman, and K. Wilder, 1997. Joint Induction of Shape Features and Tree Classifiers. IEEE Transc. on Pattern Anal. and machine Intell., 19 (11), pp: 1300- 1305. Breiman, L., 1996. Bagging.... NIST Technical Report NISTIR 5163. Cappelli, R., A. Lumini, D. Maio., and D. Maltoni, 1999. Fingerprint Classification by Direct image Partitioning. IEEE Transc. On Pattern Anal. and Machine Intell., 21 (5), pp: 402-421. Cox, D.R., 1966. Some...

  7. Bayesian network classifiers for categorizing cortical GABAergic interneurons.

    Science.gov (United States)

    Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro

    2015-04-01

    An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

  8. A Gestalt Approach to Group Supervision

    Science.gov (United States)

    Melnick, Joseph; Fall, Marijane

    2008-01-01

    The authors define and then describe the practice of group supervision. The role of creative experiment in assisting supervisees who perceive themselves as confused, moving in circles, or immobilized is described. Fictional case examples illustrate these issues in supervision. The authors posit the "good fit" of Gestalt theory and techniques with…

  9. 19 CFR 146.3 - Customs supervision.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 2 2010-04-01 2010-04-01 false Customs supervision. 146.3 Section 146.3 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) FOREIGN TRADE ZONES General Provisions § 146.3 Customs supervision. (a) Assignment of...

  10. 27 CFR 70.609 - Supervision.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 2 2010-04-01 2010-04-01 false Supervision. 70.609... From Disaster, Vandalism, or Malicious Mischief Destruction of Liquors § 70.609 Supervision. When... official or made unmarketable, the liquors shall be destroyed by suitable means under...

  11. 28 CFR 551.32 - Staff supervision.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Staff supervision. 551.32 Section 551.32 Judicial Administration BUREAU OF PRISONS, DEPARTMENT OF JUSTICE INSTITUTIONAL MANAGEMENT MISCELLANEOUS Inmate Organizations § 551.32 Staff supervision. (a) The Warden shall appoint a staff member as...

  12. 17 CFR 166.3 - Supervision.

    Science.gov (United States)

    2010-04-01

    ... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Supervision. 166.3 Section 166.3 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION CUSTOMER PROTECTION RULES § 166.3 Supervision. Each Commission registrant, except an associated person who has no...

  13. 9 CFR 146.10 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Supervision. 146.10 Section 146.10 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE... Supervision. (a) The Official State Agency may designate qualified persons as Authorized Agents to do...

  14. 36 CFR 25.3 - Supervision; suspensions.

    Science.gov (United States)

    2010-07-01

    ... 36 Parks, Forests, and Public Property 1 2010-07-01 2010-07-01 false Supervision; suspensions. 25.3 Section 25.3 Parks, Forests, and Public Property NATIONAL PARK SERVICE, DEPARTMENT OF THE INTERIOR NATIONAL MILITARY PARKS; LICENSED GUIDE SERVICE REGULATIONS § 25.3 Supervision; suspensions. (a) The...

  15. The real relationship in psychotherapy supervision.

    Science.gov (United States)

    Watkins, C Edward

    2011-01-01

    While the real relationship has long been addressed in psychoanalysis and psychotherapy, the matter of the real relationship in psychotherapy supervision has yet to receive any attention. Ample supervisory focus has indeed been given to the working alliance and transference-countertransference configuration (including parallel processes), but after a century of psychotherapy supervision, any mention whatsoever of real relationship phenomena is absent. In this paper, the following hypotheses are proposed: The real relationship (1) is a crucial component of the supervision relationship that has transtheoretical implications; (2) exists from the moment supervision begins until its end; (3) is the forever silent yet forever substantive contributor to supervisory process and outcome; (4) exerts a significant impact on (a) the development and establishment of the supervisory working alliance and (b) the unfolding and eventual utilization of the transference-countertransference experience in the supervisory situation; (5) consists of at least two dimensions in supervision--realism and genuineness--that vary along valence and magnitude continua (building on the works of Greenson and Gelso), and (6) deserves a place of eminence equal to the working alliance and transference-countertransference configuration if supervision theory, practice, and research are to be most fully informed. The possibility of using recent real relationship research in psychotherapy as a prototype to inform future research in supervision is presented, and two case examples are provided to illustrate the seeming power of real relationship phenomena in psychotherapy supervision.

  16. 21 CFR 640.62 - Medical supervision.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Medical supervision. 640.62 Section 640.62 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) BIOLOGICS ADDITIONAL STANDARDS FOR HUMAN BLOOD AND BLOOD PRODUCTS Source Plasma § 640.62 Medical supervision....

  17. Teacher Supervision Practices and Principals' Characteristics

    Science.gov (United States)

    April, Daniel; Bouchamma, Yamina

    2015-01-01

    A questionnaire was used to determine the individual and collective teacher supervision practices of school principals and vice-principals in Québec (n = 39) who participated in a research-action study on pedagogical supervision. These practices were then analyzed in terms of the principals' sociodemographic and socioprofessional characteristics…

  18. 32 CFR 552.65 - Command supervision.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 3 2010-07-01 2010-07-01 true Command supervision. 552.65 Section 552.65 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY MILITARY RESERVATIONS AND....65 Command supervision. (a) All insurance business conducted on Army installation will be...

  19. 9 CFR 145.11 - Supervision.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Supervision. 145.11 Section 145.11 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE... Supervision. (a) The Official State Agency may designate qualified persons as Authorized Agents to do...

  20. Experiencing Higher Degree Research Supervision as Teaching

    Science.gov (United States)

    Bruce, Christine; Stoodley, Ian

    2013-01-01

    This article describes higher degree research supervisors' experiences of supervision as teaching. While research education is considered central to the higher degree research experience, comparatively little is known to date of the teaching lenses adopted by supervisors as they go about their supervision. We worked with 35 supervisors engaged in…

  1. The Agile Approach with Doctoral Dissertation Supervision

    Science.gov (United States)

    Tengberg, Lars Göran Wallgren

    2015-01-01

    Several research findings conclude that many doctoral students fail to complete their studies within the allowable time frame, in part because of problems related to the research and supervision process. Surveys show that most doctoral students are generally satisfied with their dissertation supervision. However, these surveys also reveal some…

  2. Applying Services Marketing Principles to Postgraduate Supervision

    Science.gov (United States)

    Dann, Stephen

    2008-01-01

    Purpose: The paper aims to describe the application of two key service quality frameworks for improving the delivery of postgraduate research supervision. The services quality frameworks are used to identify key areas of overlap between services marketing practice and postgraduate supervision that can be used by the supervisor to improve research…

  3. Applying Services Marketing Principles to Postgraduate Supervision

    Science.gov (United States)

    Dann, Stephen

    2008-01-01

    Purpose: The paper aims to describe the application of two key service quality frameworks for improving the delivery of postgraduate research supervision. The services quality frameworks are used to identify key areas of overlap between services marketing practice and postgraduate supervision that can be used by the supervisor to improve research…

  4. The School Counselor, the Cactus, and Supervision

    Science.gov (United States)

    Boyd, John D.; Walter, Paul B.

    1975-01-01

    The authors suggest that counselor supervision is a viable way to assist school counselors in dealing with lack of professional development opportunities. Supervision can facilitate the counselor's personal and professional development and can promote counselor competencies, accountability and the improvement of guidance services and programs. (SE)

  5. A Gestalt Approach to Group Supervision

    Science.gov (United States)

    Melnick, Joseph; Fall, Marijane

    2008-01-01

    The authors define and then describe the practice of group supervision. The role of creative experiment in assisting supervisees who perceive themselves as confused, moving in circles, or immobilized is described. Fictional case examples illustrate these issues in supervision. The authors posit the "good fit" of Gestalt theory and techniques with…

  6. State Supervision and Control of Radiation Protection

    CERN Document Server

    2001-01-01

    Radiation Protection Centre is carrying state supervision and control of radiation protection. The main objective of state supervision and control of radiation protection is assessing how licensees comply with requirements of the appropriate legislation and enforcement. Summary of inspections conducted in 1999-2001 is presented.

  7. State Radiation Protection Supervision and Control

    CERN Document Server

    2002-01-01

    Radiation Protection Centre is carrying state supervision and control of radiation protection. The main objective of state supervision and control of radiation protection is assessing how licensees comply with requirements of the appropriate legislation and enforcement. Summary of inspections conducted in 2002 is presented.

  8. [RVM supervised feature extraction and Seyfert spectra classification].

    Science.gov (United States)

    Li, Xiang-Ru; Hu, Zhan-Yi; Zhao, Yong-Heng; Li, Xiao-Ming

    2009-06-01

    With recent technological advances in wide field survey astronomy and implementation of several large-scale astronomical survey proposals (e. g. SDSS, 2dF and LAMOST), celestial spectra are becoming very abundant and rich. Therefore, research on automated classification methods based on celestial spectra has been attracting more and more attention in recent years. Feature extraction is a fundamental problem in automated spectral classification, which not only influences the difficulty and complexity of the problem, but also determines the performance of the designed classifying system. The available methods of feature extraction for spectra classification are usually unsupervised, e. g. principal components analysis (PCA), wavelet transform (WT), artificial neural networks (ANN) and Rough Set theory. These methods extract features not by their capability to classify spectra, but by some kind of power to approximate the original celestial spectra. Therefore, the extracted features by these methods usually are not the best ones for classification. In the present work, the authors pointed out the necessary to investigate supervised feature extraction by analyzing the characteristics of the spectra classification research in available literature and the limitations of unsupervised feature extracting methods. And the authors also studied supervised feature extracting based on relevance vector machine (RVM) and its application in Seyfert spectra classification. RVM is a recently introduced method based on Bayesian methodology, automatic relevance determination (ARD), regularization technique and hierarchical priors structure. By this method, the authors can easily fuse the information in training data, the authors' prior knowledge and belief in the problem, etc. And RVM could effectively extract the features and reduce the data based on classifying capability. Extensive experiments show its superior performance in dimensional reduction and feature extraction for Seyfert

  9. IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST SENSITIVE CRITERION FOR C-SVM

    Directory of Open Access Journals (Sweden)

    M’hamed Bilal Abidine

    2013-11-01

    Full Text Available The growing population of elders in the society calls for a new approach in care giving. By inferring what activities elderly are performing in their houses it is possible to determine their physical and cognitive capabilities. In this paper we show the potential of important discriminative classifiers namely the Soft-Support Vector Machines (C-SVM, Conditional Random Fields (CRF and k-Nearest Neighbors (k-NN for recognizing activities from sensor patterns in a smart home environment. We address also the class imbalance problem in activity recognition field which has been known to hinder the learning performance of classifiers. Cost sensitive learning is attractive under most imbalanced circumstances, but it is difficult to determine the precise misclassification costs in practice. We introduce a new criterion for selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed criterion outperforms the state-of-the-art discriminative methods in activity recognition.

  10. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  11. An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases.

    Science.gov (United States)

    Sengur, Abdulkadir

    2008-03-01

    In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

  12. Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

    Science.gov (United States)

    Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

    2016-02-01

    The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. 基于KNN的Android智能手机微信取证方法%A KNN based forensic method of Android smartphone WeChat

    Institute of Scientific and Technical Information of China (English)

    吴熙曦; 李炳龙; 张天琪

    2014-01-01

    To solve the problem that data of WeChat is so much that data related to the case can’t be found quickly,a Android smart phone WeChat forensic method based KNN algorithm was presented.Word similarity was introduced to calculate the distance of conversations.The conversations would be represented as a vector of feature words and catego-rized with KNN algorithm to quickly find the crime-related data.The experiments verify the feasibility and accuracy of the method.%针对微信数据多,无法从中快速找到与案件相关数据的问题,提出了一种基于KNN(k-nearest neighbor)算法的Android智能手机微信取证方法。引入词语相似度计算会话间的距离,将微信会话表示成特征词的向量,用KNN算法对会话进行分类,迅速找到与犯罪有关的聊天内容,并通过实验验证了该方法的可行性与准确性。

  14. 26KNN approach to denoising f rom ALS point clouds%基于26KNN的机载点云去噪方法

    Institute of Scientific and Technical Information of China (English)

    李峰海

    2013-01-01

    Data of point clouds from ALS have huge noises w hich infence the accuracy of process-ing and occupy mounts of computer memory .Therefore denoising is highly enssential before huge point cloud processing .According to the traditional KNN method ,this paper proposed an algo-rithm called 26KNN which provides a reliable preprocessing method of huge point cloud .%机载扫描系统(ALS)点云数据中含有数量巨大的噪声点,影响数据处理精度,同时也占用了大量的内存。因此在处理点云数据前必须对超大点云进行去噪。根据传统的KNN算法,结合分块读取、存贮技术,提出基于26KNN的机载点云去噪方法,成功实现了超大点云的预处理。

  15. Performance Evaluation of Bagged RBF Classifier for Data Mining Applications

    Directory of Open Access Journals (Sweden)

    M.Govindarajan

    2013-11-01

    Full Text Available Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with radial basis function classifier as the base learner. The proposed bagged radial basis function is superior to individual approach for data mining applications in terms of classification accuracy.

  16. Is it possible to strengthen psychiatric nursing staff's clinical supervision?

    DEFF Research Database (Denmark)

    Gonge, Henrik; Buus, Niels

    2015-01-01

    AIM: To test the effects of a meta-supervision intervention in terms of participation, effectiveness and benefits of clinical supervision of psychiatric nursing staff. BACKGROUND: Clinical supervision is regarded as a central component in developing mental health nursing practices, but the evidence...... on individuals or wards already actively engaged in clinical supervision, which suggested that individuals and wards without well-established supervision practices may require more comprehensive interventions targeting individual and organizational barriers to clinical supervision....

  17. Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle.

    Science.gov (United States)

    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    2008-03-01

    This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.

  18. Supervision over technological and measurement processes

    Directory of Open Access Journals (Sweden)

    T. Karkoszka

    2011-01-01

    Full Text Available Purpose: of the paper has been an attainment of the thesis: “If there is something we can define, we can measure it. If there is something we can measure, we can analyse that. If there is something we can analyse, we can supervise that. If there is something we can supervise, we can improve that [1]”.Design/methodology/approach: used for the analysis has covered proposition of the supervision over measurement processes system that can be applied in quality assurance.Findings: of analysis are as follows: system of supervision over measurement processes performed (in compliance with real, realised in an organisation processes, can assure the achievement of the accurate and reliable results that, being the base of any feedback in any process, have fundamental meaning in making appropriate decisions.Practical implications: can be applied in case of any organisation, wanting to demonstrate the conformity of processes in the range of requirements that apply to: products, workers, natural environment and others by supervision over measurement equipment.Originality/value: of the presented paper has been obtained by working out the design of supervision over measurement system, also measurement system capability, which should be supplement for supervision over processes system in the assurance of processes and products quality.

  19. Providing effective supervision in clinical neuropsychology.

    Science.gov (United States)

    Stucky, Kirk J; Bush, Shane; Donders, Jacobus

    2010-01-01

    A specialty like clinical neuropsychology is shaped by its selection of trainees, educational standards, expected competencies, and the structure of its training programs. The development of individual competency in this specialty is dependent to a considerable degree on the provision of competent supervision to its trainees. In clinical neuropsychology, as in other areas of professional health-service psychology, supervision is the most frequently used method for teaching a variety of skills, including assessment, report writing, differential diagnosis, and treatment. Although much has been written about the provision of quality supervision in clinical and counseling psychology, very little published guidance is available regarding the teaching and provision of supervision in clinical neuropsychology. The primary focus of this article is to provide a framework and guidance for the development of suggested competency standards for training of neuropsychological supervisors, particularly at the residency level. In this paper we outline important components of supervision for neuropsychology trainees and suggest ways in which clinicians can prepare for supervisory roles. Similar to Falender and Shafranske (2004), we propose a competency-based approach to supervision that advocates for a science-informed, formalized, and objective process that clearly delineates the competencies required for good supervisory practice. As much as possible, supervisory competencies are related to foundational and functional competencies in professional psychology, as well as recent legislative initiatives mandating training in supervision. It is our hope that this article will foster further discussion regarding this complex topic, and eventually enhance training in clinical neuropsychology.

  20. Sentiment Analysis of Twitter tweets using supervised classification technique

    Directory of Open Access Journals (Sweden)

    Pranav Waykar

    2016-05-01

    Full Text Available Making use of social media for analyzing the perceptions of the masses over a product, event or a person has gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets on users' area of interest and analyze them. The extracted tweets are then segregated as positive, negative and neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the collected tweets to convert all letters to lowercase, eliminate special characters etc. which makes the classification more efficient; the processed tweets are classified using a supervised classification technique. We make use of Naive Bayes classifier to segregate the tweets as positive, negative and neutral. We use a set of sample tweets to train the classifier. The percentage of the tweets in each category is then computed and the result is represented graphically. The result can be used further to gain an insight into the views of the people using Twitter about a particular topic that is being searched by the user. It can help corporate houses devise strategies on the basis of the popularity of their product among the masses. It may help the consumers to make informed choices based on the general sentiment expressed by the Twitter users on a product

  1. Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech.

    Science.gov (United States)

    Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

    2015-12-25

    In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one.

  2. Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

    Science.gov (United States)

    Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

    2015-01-01

    In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757

  3. Extracting microRNA-gene relations from biomedical literature using distant supervision.

    Science.gov (United States)

    Lamurias, Andre; Clarke, Luka A; Couto, Francisco M

    2017-01-01

    Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

  4. Extracting microRNA-gene relations from biomedical literature using distant supervision

    Science.gov (United States)

    Clarke, Luka A.; Couto, Francisco M.

    2017-01-01

    Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel. PMID:28263989

  5. Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

    Directory of Open Access Journals (Sweden)

    Lei Deng

    2015-01-01

    Full Text Available A novel approach is proposed for classifying the polarimetric SAR (PolSAR data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

  6. Social constructionism and supervision: experiences of AAMFT supervisors and supervised therapists.

    Science.gov (United States)

    Hair, Heather J; Fine, Marshall

    2012-10-01

    A phenomenological research process was used to investigate the supervision experience for supervisors and therapists when supervisors use a social constructionist perspective. Participants of the one-to-one interviews were six AAMFT Approved Supervisors and six therapists providing counseling to individuals, couples and families. The findings suggest supervisors were committed to their self-identified supervision philosophy and intentionally sought out congruence between epistemology and practice. The shared experience of therapists indicates they associated desirable supervision experiences with their supervisors' social constructionist perspective. Our findings also indicated that supervisors' and therapists' understanding of social constructionism included the more controversial concepts of agency and extra-discursiveness. This research has taken an empirical step in the direction of understanding what the social constructionist supervision experience is like for supervisors and therapists. Our findings suggest a linkage between epistemology and supervision practice and a satisfaction with the supervision process. © 2012 American Association for Marriage and Family Therapy.

  7. 75 FR 707 - Classified National Security Information

    Science.gov (United States)

    2010-01-05

    ... National Security Information Memorandum of December 29, 2009--Implementation of the Executive Order ``Classified National Security Information'' Order of December 29, 2009--Original Classification Authority #0... 13526 of December 29, 2009 Classified National Security Information This order prescribes a...

  8. Classifier Assignment by Corpus-based Approach

    CERN Document Server

    Sornlertlamvanich, V; Meknavin, S; Sornlertlamvanich, Virach; Pantachat, Wantanee; Meknavin, Surapant

    1994-01-01

    This paper presents an algorithm for selecting an appropriate classifier word for a noun. In Thai language, it frequently happens that there is fluctuation in the choice of classifier for a given concrete noun, both from the point of view of the whole spe ech community and individual speakers. Basically, there is no exect rule for classifier selection. As far as we can do in the rule-based approach is to give a default rule to pick up a corresponding classifier of each noun. Registration of classifier for each noun is limited to the type of unit classifier because other types are open due to the meaning of representation. We propose a corpus-based method (Biber, 1993; Nagao, 1993; Smadja, 1993) which generates Noun Classifier Associations (NCA) to overcome the problems in classifier assignment and semantic construction of noun phrase. The NCA is created statistically from a large corpus and recomposed under concept hierarchy constraints and frequency of occurrences.

  9. Effective School Management and Supervision: Imperative for ...

    African Journals Online (AJOL)

    To provide quality education requires effective management of the human and material resources in the industry. ... examines the twin concepts of school management and supervision as the vital factors in providing quality education services.

  10. Factors affecting frontline workers' satisfaction with supervision.

    Science.gov (United States)

    Noelker, Linda S; Ejaz, Farida K; Menne, Heather L; Bagaka's, Joshua G

    2009-02-01

    Objective. This research was guided by a stress and support model to examine the effects of frontline workers' background characteristics, personal stressors, job-related stressors, and workplace support on satisfaction with supervision. Method. Survey data were collected from 644 workers in 49 long-term-care settings that employed them. Regression analysis were used to determine the effects of worker level and then Hierarchical Linear Modeling (HLM) and organizational factors on the outcome. Results. Although all four variable categories made significant contributions to explaining satisfaction with supervision, the most powerful were personal stressors and job-related stressors. Results from HLM analysis showed frontline workers in nursing homes and those employed in for-profit organizations had lower levels of satisfaction with supervision. Discussion. Findings suggest organizational interventions to address workers' financial and health issues and management practices such as better training programs and peer mentoring could enhance workers' satisfaction with supervision.

  11. Results of Evolution Supervised by Genetic Algorithms

    CERN Document Server

    Jäntschi, Lorentz; Bălan, Mugur C; Sestraş, Radu E

    2010-01-01

    A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.

  12. The Cryogenic Supervision System in NSRRC

    CERN Document Server

    Li, Hsing-Chieh; Chiou, Wen-Song; Hsiao, Feng-Zone; Tsai, Zong-Da

    2005-01-01

    The helium cryogenic system in NSRRC is a fully automatic PLC system using the Siemens SIMATIC 300 controller. Modularization in both hardware and software makes it easy in the program reading, the system modification and the problem debug. Based on the Laview program we had developed a supervision system taking advantage of the Internet technology to get system's real-time information in any place. The functions of this supervision system include the real-time data accessing with more than 300 digital/analog signals, the data restore, the history trend display, and the human machine interface. The data is accessed via a Profibus line connecting the PLC system and the supervision system with a maximum baud rate 1.5 Mbit/s. Due to this supervision system, it is easy to master the status of the cryogenic system within a short time and diagnose the problem.

  13. Supervision of Curriculum at the Middle Level.

    Science.gov (United States)

    Kienapfel, Bruce

    1984-01-01

    Effective curriculum supervision is essential to a good middle school and curriculum program, and principals can do this job through preparation, curriculum review and development, taking advantage of supervisory opportunities, and curriculum evaluation. (DCS)

  14. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

  15. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  16. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  17. Exploring My Style of Teacher Supervision

    OpenAIRE

    2009-01-01

    This is a report on a pilot study regarding language teacher supervision styles. The author holds a certificate in language program administration, and the pilot involved teachers of the Japanese language one of whom was also involved in the certificate program. The teachers teach at an American language institute and they agreed to be observed by the author, following procedures detailed in clinical supervision. After the lesson observation, a one-on-one conference was held with the author a...

  18. Integrated Financial Supervision: Experiences in Selected Countries

    OpenAIRE

    Edgardo Demaestri; Diego Sourrouille

    2003-01-01

    This paper represents one of the first comparative analyses of experiences of integrated supervision. It discusses how several countries around the world have developed the processes of integrating financial regulation and supervision, and covers numerous relevant technical issues as well as the policy options. It describes the scope of the activities, institutions, responsibilities, and regulatory powers that integrated supervisors are expected to cover. Issues related to the organizational ...

  19. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily...

  20. On psychoanalytic supervision as signature pedagogy.

    Science.gov (United States)

    Watkins, C Edward

    2014-04-01

    What is signature pedagogy in psychoanalytic education? This paper examines that question, considering why psychoanalytic supervision best deserves that designation. In focusing on supervision as signature pedagogy, I accentuate its role in building psychoanalytic habits of mind, habits of hand, and habits of heart, and transforming theory and self-knowledge into practical product. Other facets of supervision as signature pedagogy addressed in this paper include its features of engagement, uncertainty, formation, and pervasiveness, as well as levels of surface, deep, and implicit structure. Epistemological, ontological, and axiological in nature, psychoanalytic supervision engages trainees in learning to do, think, and value what psychoanalytic practitioners in the field do, think, and value: It is, most fundamentally, professional preparation for competent, "good work." In this paper, effort is made to shine a light on and celebrate the pivotal role of supervision in "making" or developing budding psychoanalysts and psychoanalytic psychotherapists. Now over a century old, psychoanalytic supervision remains unparalleled in (1) connecting and integrating conceptualization and practice, (2) transforming psychoanalytic theory and self-knowledge into an informed analyzing instrument, and (3) teaching, transmitting, and perpetuating the traditions, practice, and culture of psychoanalytic treatment.

  1. 9 CFR 355.31 - Supervision by inspector.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Supervision by inspector. 355.31..., CERTIFICATION, AND IDENTIFICATION AS TO CLASS, QUALITY, QUANTITY, AND CONDITION Supervision § 355.31 Supervision... filled in whole or in part and no such label shall be affixed thereto except under the supervision of...

  2. 28 CFR 2.94 - Supervision reports to Commission.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision reports to Commission. 2.94 Section 2.94 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT... Parolees § 2.94 Supervision reports to Commission. An initial supervision report to confirm...

  3. 10 CFR 34.46 - Supervision of radiographers' assistants.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 1 2010-01-01 2010-01-01 false Supervision of radiographers' assistants. 34.46 Section 34... REQUIREMENTS FOR INDUSTRIAL RADIOGRAPHIC OPERATIONS Radiation Safety Requirements § 34.46 Supervision of... personal supervision of a radiographer. The personal supervision must include: (a) The...

  4. Opportunities to Learn Scientific Thinking in Joint Doctoral Supervision

    Science.gov (United States)

    Kobayashi, Sofie; Grout, Brian W.; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervision. While joint supervision has become widely…

  5. 28 CFR 2.207 - Supervision reports to Commission.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision reports to Commission. 2.207 Section 2.207 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT....207 Supervision reports to Commission. A regular supervision report shall be submitted to...

  6. 48 CFR 52.247-12 - Supervision, Labor, or Materials.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 2 2010-10-01 2010-10-01 false Supervision, Labor, or....247-12 Supervision, Labor, or Materials. As prescribed in 47.207-5(b), insert a clause substantially... when the contractor is required to furnish supervision, labor, or materials: Supervision, Labor,...

  7. 28 CFR 810.1 - Supervision contact requirements.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Supervision contact requirements. 810.1 Section 810.1 Judicial Administration COURT SERVICES AND OFFENDER SUPERVISION AGENCY FOR THE DISTRICT OF COLUMBIA COMMUNITY SUPERVISION: ADMINISTRATIVE SANCTIONS § 810.1 Supervision contact requirements. If...

  8. Exploring Supervisor and Supervisee Experiences of Triadic Supervision

    Science.gov (United States)

    Derrick, Emily C.

    2010-01-01

    This dissertation research focused on supervisor and supervisee experiences within the triadic supervision triad. Triadic supervision is an emerging method of supervision within counselor education. It is fast becoming the preferred mode of supervision in counselor education programs. Unfortunately, there is very little research to support the…

  9. A Model for Using Triadic Supervision in Counselor Preparation Programs

    Science.gov (United States)

    Lawson, Gerard; Hein, Serge F.; Getz, Hildy

    2009-01-01

    The Council for Accreditation of Counseling and Related Educational Programs (2001) has approved the use of triadic supervision as an alternative to individual supervision in clinical instruction. However, literature describing this mode of supervision is very limited. A model for triadic supervision is described, including presession planning,…

  10. SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm.

    Science.gov (United States)

    Du, Pufeng; Cao, Shengjiao; Li, Yanda

    2009-11-21

    The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.

  11. Automated detection of pulmonary nodules in CT: false positive reduction by combining multiple classifiers

    Science.gov (United States)

    Suárez-Cuenca, Jorge Juan; Guo, Wei; Li, Qiang

    2011-03-01

    The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a CAD system for pulmonary nodule detection in CT. We employed CT cases in the publicly available lung image database consortium (LIDC) dataset, which included 85 CT cases with 110 nodules. We first used six individual classifiers for nodule detection in CT, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and three types of support vector machines (SVM). Five informationfusion methods were then employed to combine the classifiers' outputs for improving detection performance. The five combination methods included two supervised (likelihood ratio method and neural network) and three unsupervised ones (the mean, the product, and the majority-vote of the output scores from the six individual classifiers). Leave-one-caseout was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80 %, the numbers of false positives per case for the six individual classifiers were 6.1 for LDA, 19.9 for QDA, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.35 for SVM-ANOVA; the numbers of false positives per case for the five combination methods were 3.4 for the majority-vote rule, 6.2 for the mean, 5.7 for the product, 9.7 for the neural network, and 28.1 for the likelihood ratio method. The majority-vote rule achieved higher performance levels than other combination methods. It also achieved higher performance than the best individual classifier, which is not the case for other combination methods.

  12. SAR Ice Image Classification Using Parallelepiped Classifier Based on Gram-Schmidt Spectral Technique

    Directory of Open Access Journals (Sweden)

    A.Vanitha

    2013-05-01

    Full Text Available Synthetic Aperture Radar (SAR is a special type of imaging radar that involves advanced technology and complex data processing to obtain de tailed images from the lake surface. Lake ice typically reflects more of the radar energy emi tted by the sensor than the surrounding area, which makes it easy to distinguish between the wate r and the ice surface. In this research work, SAR images are used for ice classification based on supervised and unsupervised classification algorithms. In the pre-processing stage, Hue satura tion value (HSV and Gram–Schmidt spectral sharpening techniques are applied for shar pening and resampling to attain high- resolution pixel size. Based on the performance eva luation metrics it is proved that Gram- Schmidt spectral sharpening performs better than sh arpening the HSV between the boundaries. In classification stage, Gram–Schmidt spectral tech nique based sharpened SAR images are used as the input for classifying using parallelepiped a nd ISO data classifier. The performances of the classifiers are evaluated with overall accuracy and kappa coefficient. From the experimental results, ice from water is classified more accurately in the parallelepiped supervised classification algorithm.

  13. Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

    Science.gov (United States)

    Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

    2014-11-01

    Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

  14. Multicultural supervision: lessons learned about an ongoing struggle.

    Science.gov (United States)

    Christiansen, Abigail Tolhurst; Thomas, Volker; Kafescioglu, Nilufer; Karakurt, Gunnur; Lowe, Walter; Smith, William; Wittenborn, Andrea

    2011-01-01

    This article examines the experiences of seven diverse therapists in a supervision course as they wrestled with the real-world application of multicultural supervision. Existing literature on multicultural supervision does not address the difficulties that arise in addressing multicultural issues in the context of the supervision relationship. The experiences of six supervisory candidates and one mentoring supervisor in addressing multicultural issues in supervision are explored. Guidelines for conversations regarding multicultural issues are provided.

  15. Group Supervision in Graduate Education: A Process of Supervision Skill Development and Text Improvement

    Science.gov (United States)

    Samara, Akylina

    2006-01-01

    This paper is an investigation of group supervision of the Master of Education thesis at the University of Bergen, Norway. Four recorded group supervision sessions are analysed. The group participants are five students and three supervisors. The sessions are analysed from a qualitative, phenomenological perspective. The results show that group…

  16. Classroom Supervision and Informal Analysis of Behavior. A Manual for Supervision.

    Science.gov (United States)

    Hull, Ray; Hansen, John

    This manual for supervision addresses itself to those with responsibility for helping teachers develop into skilled professionals through use of a rational plan of feedback and assistance. It describes the supervision cycle and outline simple and practical techniques to collect effective data that will assist the classroom teacher. The manual has…

  17. Group Supervision in Graduate Education: A Process of Supervision Skill Development and Text Improvement

    Science.gov (United States)

    Samara, Akylina

    2006-01-01

    This paper is an investigation of group supervision of the Master of Education thesis at the University of Bergen, Norway. Four recorded group supervision sessions are analysed. The group participants are five students and three supervisors. The sessions are analysed from a qualitative, phenomenological perspective. The results show that group…

  18. 22 CFR 125.3 - Exports of classified technical data and classified defense articles.

    Science.gov (United States)

    2010-04-01

    ... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Exports of classified technical data and... IN ARMS REGULATIONS LICENSES FOR THE EXPORT OF TECHNICAL DATA AND CLASSIFIED DEFENSE ARTICLES § 125.3 Exports of classified technical data and classified defense articles. (a) A request for authority...

  19. Comparison of wheat classification accuracy using different classifiers of the image-100 system

    Science.gov (United States)

    Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.

    1981-01-01

    Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.

  20. An adaptation strategy of using LDA classifier for EMG pattern recognition.

    Science.gov (United States)

    Zhang, Haoshi; Zhao, Yaonan; Yao, Fuan; Xu, Lisheng; Shang, Peng; Li, Guanglin

    2013-01-01

    The time-varying character of myoelectric signal usually causes a low classification accuracy in traditional supervised pattern recognition method. In this work, an unsupervised adaptation strategy of linear discriminant analysis (ALDA) based on probability weighting and cycle substitution was suggested in order to improve the performance of electromyography (EMG)-based motion classification in multifunctional myoelectric prostheses control in changing environment. The adaptation procedure was firstly introduced, and then the proposed ALDA classifier was trained and tested with surface EMG recordings related to multiple motion patterns. The accuracies of the ALDA classifier and traditional LDA classifier were compared when the EMG recordings were added with different degrees of noise. The experimental results showed that compared to the LDA method, the suggested ALDA method had a better performance in improving the classification accuracy of sEMG pattern recognition, in both stable situation and noise added situation.

  1. Intuitive expertise in ICT graduate supervision

    Directory of Open Access Journals (Sweden)

    Jill Jameson

    2002-12-01

    Full Text Available Intuitive expertise in the application of advanced interdisciplinary facilitation is the subject of this personal reflection on the graduate supervisory style of Professor David Squires in computers in education. This single-case reflective study examines the characteristics of effective supervision observed during masters and doctoral supervision at King's College in the years 1990-9. Interdisciplinarity in ICT graduate studies particularly requires a fluency of supervisory expertise in enabling supervisees to combine multiple complex perspectives from a number of fields of knowledge. Intuitive combinatory aspects of supervision are highlighted in this reflection on the role carried out by an academic expert in facilitating student success. This is examined from a perspective incorporating affective as well as intellectual elements, informed by characteristics identified in professional sports and performing arts coaching/mentoring. Key characteristics comprising a model of intuitive expertise in ICT graduate supervision were outlined. The resultant portrait aims to complement existing literature on graduate supervision, with reference to the field of ICTI computers in education relating to student hypermedia composition.

  2. Appreciative Perspectives on Supervision in Social Work

    Directory of Open Access Journals (Sweden)

    Elena UNGURU

    2017-06-01

    Full Text Available Supervision is considered to be an extremly important stage in the professional development of the specialists in the fields with asistential nature, such as: psychotherapy, coaching, personal development, counselling, etc. In Romania, the supervision of social services has become compulsory through the Order 288/2006. A great part of the content of standards for the case management, including those referred to supervision, namely to the theoretical and methodological development of some teachers, such as: Professor PhD Ştefan Cojocaru, Professor PhD Ana Muntean, Professor PhD Elena Zamfir. In this review, we will synthesize a part of Professor Ştefan Cojocaru’s contribution to the development of the field of appreciative supervision, as it is presented in the volume Appreciative methods in social work. Survey, supervision and case management, published with Polirom Publishing House in 2005. We will aim to also synthesize a series of echos of his work in the Romanian scientific literature.

  3. Pavement Crack Classifiers: A Comparative Study

    Directory of Open Access Journals (Sweden)

    S. Siddharth

    2012-12-01

    Full Text Available Non Destructive Testing (NDT is an analysis technique used to inspect metal sheets and components without harming the product. NDT do not cause any change after inspection; this technique saves money and time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM extracts features from these images. Extracted features are then fed into the classifiers which classifies them into images with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM and Linear classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best classifier for the given data is chosen.

  4. Comparing different classifiers for automatic age estimation.

    Science.gov (United States)

    Lanitis, Andreas; Draganova, Chrisina; Christodoulou, Chris

    2004-02-01

    We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.

  5. Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

    Science.gov (United States)

    Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

    2016-07-01

    Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

  6. Project Supervision – An Engineering Approach

    DEFF Research Database (Denmark)

    Paulsen, Rasmus Reinhold; Larsen, Rasmus; Ersbøll, Bjarne Kjær

    2011-01-01

    of their master thesis writing. The supervision principles are: Ownership: The student should feel that their project is their own. Ideally, they should formulate the project themselves. Write early: We strongly encourage the students to write and generate figures and images already from the first week......For more than twenty years, a group based supervision strategy has been used when supervising engineering bachelor- and master thesis students at our research group. In recent years, we have formalised the approach and used our industry experience to create a very successful framework for project...... at all meetings. Weekly meetings are scheduled to be at a specific day at a specific place for the entire process....

  7. Multi-Instance Learning from Supervised View

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2006-01-01

    In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances,and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners.

  8. Core ethical issues of clinical nursing supervision.

    Science.gov (United States)

    Berggren, Ingela; Barbosa da Silva, António; Severinsson, Elisabeth

    2005-03-01

    Clinical nursing supervision enables supervisees to reflect on ethically difficult caring situations, thereby strengthening their professional identity, integrating nursing theory and practice, and leading to the development of ethical competence. The aim of this study was to develop an understanding of the core ethical issues of clinical nursing supervision, using previous research as well as philosophical analysis of the theories of three moral philosophers: Harald Ofstad, Richard Hare and Carol Gilligan. The ultimate aim of this study was to develop a general model for ethical decision-making and to establish its relevance for clinical nursing supervision. The findings highlight four important values for the development of a basis for ethical decision-making. These values are caring, dignity, responsibility and virtue.

  9. Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

    Science.gov (United States)

    Kumar, Mukesh; Rath, Nitish Kumar; Rath, Santanu Kumar

    2016-04-01

    Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Guidelines for clinical supervision in health service psychology.

    Science.gov (United States)

    2015-01-01

    This document outlines guidelines for supervision of students in health service psychology education and training programs. The goal was to capture optimal performance expectations for psychologists who supervise. It is based on the premises that supervisors (a) strive to achieve competence in the provision of supervision and (b) employ a competency-based, meta-theoretical approach to the supervision process. The Guidelines on Supervision were developed as a resource to inform education and training regarding the implementation of competency-based supervision. The Guidelines on Supervision build on the robust literatures on competency-based education and clinical supervision. They are organized around seven domains: supervisor competence; diversity; relationships; professionalism; assessment/evaluation/feedback; problems of professional competence, and ethical, legal, and regulatory considerations. The Guidelines on Supervision represent the collective effort of a task force convened by the American Psychological Association (APA) Board of Educational Affairs (BEA). PsycINFO Database Record (c) 2015 APA, all rights reserved.

  11. Incremental Supervised Subspace Learning for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.

  12. EEM{sup TM} wireless supervision

    Energy Technology Data Exchange (ETDEWEB)

    Bilic, H. [Ericsson-Nikola Tesla d.d. Zagreb (Croatia)

    2000-07-01

    By adding the GSM network to the communication level of Energy Management systems, energy operating centres (EOC) can offer wireless access to the supervised equipment. Furthermore EOC can profit from rapid service development in the GSM networks. With implementation of GPRS to the GSM network EOC can instantly offer wireless access to external IP based networks such as Internet and corporate Intranets. The author describes architecture and key characteristic of Ericsson EnergyMaster{sup TM} (EEM{sup TM}) system for Energy Management, how and where to implement wireless supervision, wireless access to IP addresses and also how to implement new services provided by the GSM network. (orig.)

  13. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  14. The application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancers.

    Science.gov (United States)

    Moss, G P; Shah, A J; Adams, R G; Davey, N; Wilkinson, S C; Pugh, W J; Sun, Y

    2012-01-23

    Discriminant analysis (DA) has previously been shown to allow the proposal of simple guidelines for the classification of 73 chemical enhancers of percutaneous absorption. Pugh et al. employed DA to classify such enhancers into simple categories, based on the physicochemical properties of the enhancer molecules (Pugh et al., 2005). While this approach provided a reasonable accuracy of classification it was unable to provide a consistently reliable estimate of enhancement ratio (ER, defined as the amount of hydrocortisone transferred after 24h, relative to control). Machine Learning methods, including Gaussian process (GP) regression, have recently been employed in the prediction of percutaneous absorption of exogenous chemicals (Moss et al., 2009; Lam et al., 2010; Sun et al., 2011). They have shown that they provide more accurate predictions of these phenomena. In this study several Machine Learning methods, including the K-nearest-neighbour (KNN) regression, single layer networks, radial basis function networks and the SVM classifier were applied to an enhancer dataset reported previously. The SMOTE sampling method was used to oversample chemical compounds with ER>10 in each training set in order to improve estimation of GP and KNN. Results show that models using five physicochemical descriptors exhibit better performance than those with three features. The best classification result was obtained by using the SVM method without dealing with imbalanced data. Following over-sampling, GP gives the best result. It correctly assigned 8 of the 12 "good" (ER>10) enhancers and 56 of the 59 "poor" enhancers (ERMachine Learning methods are that they can provide more accurate classification of enhancer type with fewer false-positive results and that, unlike discriminant analysis, they are able to make predictions of enhancer ability.

  15. A machine learned classifier for RR Lyrae in the VVV survey

    Science.gov (United States)

    Elorrieta, Felipe; Eyheramendy, Susana; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K.; Minniti, Dante

    2016-11-01

    Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (i.e., fundamental-mode pulsators), from the 106-107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

  16. Contributions to the Analysis and the Supervision of a Thermal Power Plant

    Directory of Open Access Journals (Sweden)

    Lakhoua M.N

    2016-02-01

    Full Text Available Supervision systems play an important role in industry mainly due to the increasing demand for product quality and high efficiency, and to the growing integration of automatic control systems in technical processes. In fact, the supervision system has a great number of components and interconnections, and it is difficult to describe and understand its behavior. Furthermore, the supervision system in industrial plants, implemented in supervisory control and data acquisition (SCADA software, must undertake, at least, the following three main tasks: monitoring, control and fault tolerance. So it can be classified as a complex system. The objective of this paper is to show interests of the use of functional analysis techniques such as SADT (Structured Analysis and Design Technique and SA-RT (Structured Analysis Real Time for the design of supervisory systems. This is why we present a general model of analysis and supervision of production systems. This model was based on the one hand on the functional analysis (FA and on the other hand on the SCADA system.

  17. A review of learning vector quantization classifiers

    CERN Document Server

    Nova, David

    2015-01-01

    In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.

  18. NEED OF FINANCIAL INSTITUTIONS SUPERVISION THROUGH AN SINGLE FRAMEWORK OF MACRO-PRUDENTIAL SUPERVISION

    Directory of Open Access Journals (Sweden)

    MEDAR LUCIAN-ION

    2013-12-01

    Full Text Available Joint Committee of the European Supervisory Authorities required Member States to implement new macro-prudential indicators.through national authorities of prudential supervision will be perform activities concerning the supplementary supervision of credit institutions, insurance companies or reinsurance companies, investment services firms and investment management firms, from a financial conglomerate. The most popular ways that give stability to the financial system are related to normal functioning of markets, to ensure implementation of payments in the economy and especially achieving a quality financial intermediation. Activities concerning macroprudential supervision concern, first of all, of managerial strengthening of internal control, assessment and management of risks

  19. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  20. ARABIC PART OF SPEECH TAGGING USING K-NEAREST NEIGHBOUR AND NAIVE BAYES CLASSIFIERS COMBINATION

    Directory of Open Access Journals (Sweden)

    Rund Mahafdah

    2014-01-01

    Full Text Available Part Of Speech (POS tagging forms the important preprocessing step in many of the natural language processing applications such as text summarization, question answering and information retrieval system. It is the process of classifying every word in a given context to its appropriate part of speech. Different POS tagging techniques in the literature have been developed and experimented. Currently, it is well known that some POS tagging models are not performing well on the Quranic Arabic due to the complexity of the Quranic Arabic text. This complexity presents several challenges for POS tagging such as high ambiguity, data sparseness and large existence of unknown words. With this in mind, the main problem here is to find out how existing and efficient methods perform in Arabic and how can Quranic corpus be utilized to produce an efficient framework for Arabic POS tagging. We propose a classifiers combination experimental framework for Arabic POS tagger, by selecting two best diverse probabilistic classifiers used in numerous works in non-Arabic language; namely K-Nearest Neighbour (KNN and Naive Bayes (NB. The Majority voting is used here as the combination strategy to exploit classifiers advantages. In addition, an in-depth study has been conducted on a large list of features for exploiting effective features and investigating their role in enhancing the performance of POS taggers for the Quranic Arabic. Hence, this study aims to efficiently integrate different feature sets and tagging algorithms to synthesize more accurate POS tagging procedure. The data used in this study is the Arabic Quranic Corpus, an annotated linguistic resource consisting of 77,430 words with Arabic grammar, syntax and morphology for each word in the Holy Quran. The highest accuracy in the results achieved is 98.32%, which can be a significant enhancement for the state-of-the-art for Arabic Quranic text. The most effective features that yield this accuracy are a

  1. Test of spectral/spatial classifier

    Science.gov (United States)

    Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

    1977-01-01

    The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

  2. Obtención de polvos cerámicos de BNKT-KNN por el método Pechini

    Directory of Open Access Journals (Sweden)

    Yasnó, J. P.

    2013-10-01

    Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3]or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.

  3. Obtención de polvos cerámicos de BNKT-KNN por el método Pechini

    Directory of Open Access Journals (Sweden)

    Yasnó, J. P.

    2013-08-01

    Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.

  4. Theory of Multiple Intelligences at Teacher Supervision

    Directory of Open Access Journals (Sweden)

    İzzet Döş

    2012-07-01

    Full Text Available This study aims to determine views of teachers and supervisors related to the multiple intelligences in students’ learning that they took into consideration in the evaluation of teachers during lesson supervision. The study was conducted with 5 supervisors who work at Kahramanmaraş provincial directorate of national education and 10 teachers who work at primary schools in the centre of Kahramanmaraş in 2011-2012 year. Data was gathered with the help of interview form consisting of five open-ended questions. In the analysis of the data content analysis which is one of the qualitative research methods. According to the results of the analysis, it has been found that usage of multiple intelligences theory in the evaluation students’ learning during supervision enabled them to evaluate students’ learning in a more detailed way. It also made it possible for the supervisors to examine supervision evaluations at different levels. It was also mentioned that supervisions made according to multiple intelligence theory has some limitations.

  5. On Restraint of and Supervision over Power

    Institute of Scientific and Technical Information of China (English)

    DONG YUNHU

    2007-01-01

    @@ Power is the nucleus of social and political life. Effective restraint on and supervision over the operation of power is the fundamental way of preventing corruption of power, which at the same time constitutes an important indication to democracy and its development in a given country.

  6. Peer Supervision: Toward More Effective Training.

    Science.gov (United States)

    Wagner, Carol A.; Smith, James P., Jr.

    1979-01-01

    Discusses peer supervision as a vehicle for increasing student responsibility for self-assessment and peer assessment, and increasing independence and interdependence among students for professional and personal growth. The article also explains a model of a master's degree program in counseling and includes rationale, definition, implementation,…

  7. Case Studies in Middle Management Supervision

    Science.gov (United States)

    White, Lori S.

    2011-01-01

    This chapter presents a series of supervision-related case studies of situations that midlevel managers might face. Individuals enrolled in a midlevel management professional development course recommended the topics selected for this chapter. Drawing upon her experience teaching the course, the author selected four case studies that individuals…

  8. 19 CFR 111.28 - Responsible supervision.

    Science.gov (United States)

    2010-04-01

    ... TREASURY CUSTOMS BROKERS Duties and Responsibilities of Customs Brokers § 111.28 Responsible supervision... the broker intends to transact customs business, a list of the names of persons currently employed by... the broker already has a permit, and before the broker begins to transact customs business as a...

  9. Magazine Picture Collage in Group Supervision

    Science.gov (United States)

    Shepard, Blythe C.; Guenette, Francis L.

    2010-01-01

    A magazine picture collage activity was used with three female counsellor education students as a vehicle to support them in processing their experience as counsellors in training. The use of magazine picture collage in group supervision is described, and the benefits and challenges are presented. The collages served as jumping-off points for…

  10. 19 CFR 19.34 - Customs supervision.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 1 2010-04-01 2010-04-01 false Customs supervision. 19.34 Section 19.34 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY CUSTOMS WAREHOUSES, CONTAINER STATIONS AND CONTROL OF MERCHANDISE THEREIN Space Bonded for the Storage...

  11. Cybersupervision: Conducting Supervision on the Information Superhighway.

    Science.gov (United States)

    Coursol, Diane

    The internship experience is an integral part of the graduate program for counselor education students. The APA Code of Ethics and Standards of Practice and the ACPA code of ethics require that students receive regular supervision from site and faculty supervisors during the practicum and internship experiences. However, when student counselors…

  12. Supporting Placement Supervision in Clinical Exercise Physiology

    Science.gov (United States)

    Sealey, Rebecca M.; Raymond, Jacqueline; Groeller, Herb; Rooney, Kieron; Crabb, Meagan; Watt, Kerrianne

    2015-01-01

    The continued engagement of the professional workforce as supervisors is critical for the sustainability and growth of work-integrated learning activities in university degrees. This study investigated factors that influence the willingness and ability of clinicians to continue to supervise clinical exercise physiology work-integrated learning…

  13. 27 CFR 24.30 - Supervision.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Supervision. 24.30 Section 24.30 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU, DEPARTMENT OF THE TREASURY LIQUORS WINE Administrative and Miscellaneous Provisions Authorities § 24.30...

  14. Remote Video Supervision in Adapted Physical Education

    Science.gov (United States)

    Kelly, Luke; Bishop, Jason

    2013-01-01

    Supervision for beginning adapted physical education (APE) teachers and inservice general physical education teachers who are learning to work with students with disabilities poses a number of challenges. The purpose of this article is to describe a project aimed at developing a remote video system that could be used by a university supervisor to…

  15. Spirituality and School Counselor Education and Supervision

    Science.gov (United States)

    Gallo, Laura L.

    2014-01-01

    Spirituality is an area that has not received a great deal of attention in supervision, yet it can have substantial effects on the counseling process. A definition of spirituality that allows for a variety of worldviews can be useful to both counselor and client as it helps strengthen the counseling relationship and lessen differences between…

  16. Performance Monitoring Applied to System Supervision

    Directory of Open Access Journals (Sweden)

    Bertille Somon

    2017-07-01

    Full Text Available Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL performance problem.

  17. Computer Monitor Supervision: A Clinical Note.

    Science.gov (United States)

    Scherl, Charles R.; Haley, Jay

    2000-01-01

    Presents communication procedures for supervisors and therapy trainees that have been developed as a result of the use of computer technology. Using the computer as a supervision tool, therapy can be influenced by the supervisor while minimizing disruption. Successes and pitfalls in a master's level practicum course in family therapy are…

  18. Computer Monitor Supervision: A Clinical Note.

    Science.gov (United States)

    Scherl, Charles R.; Haley, Jay

    2000-01-01

    Presents communication procedures for supervisors and therapy trainees that have been developed as a result of the use of computer technology. Using the computer as a supervision tool, therapy can be influenced by the supervisor while minimizing disruption. Successes and pitfalls in a master's level practicum course in family therapy are…

  19. Enhancing Adult Learning in Clinical Supervision

    Science.gov (United States)

    Goldman, Stuart

    2011-01-01

    Objective/Background: For decades, across almost every training site, clinical supervision has been considered "central to the development of skills" in psychiatry. The crucial supervisor/supervisee relationship has been described extensively in the literature, most often framed as a clinical apprenticeship of the novice to the master craftsman.…

  20. Supervision That Improves Teaching: Strategies and Techniques.

    Science.gov (United States)

    Sullivan, Susan; Glanz, Jeffrey

    This book offers a plan for improved classroom practice through the supervisory process. It includes hands-on practices for developing a personalized supervision strategy, research-based and empirically tested strategies, field-tested tools and techniques for qualitative and quantitative observation, a comprehensive resource of traditional and…

  1. Instructional Supervision: The Policy-Practice Rift.

    Science.gov (United States)

    Tunison, Scott D.

    2001-01-01

    Discusses the results of a recent Canadian study in which the perceptions of superintendents, principals, and teachers were analyzed and compared in order to determine the actual level of agreement between the practices for instructional supervision outlined in the school division's policy manual and the actual practice in the schools governed by…

  2. On Training Targets for Supervised Speech Separation

    Science.gov (United States)

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation. PMID:25599083

  3. Making Supervision Relationships Accountable: Graduate Student Logs.

    Science.gov (United States)

    Yeatman, Anna

    1995-01-01

    Graduate student journals of research projects and their supervision are suggested as a means of structuring the supervisory process, making it more accountable, and facilitating students' successful completion of their academic and research tasks. However, the method also requires skill in successful thesis production on the supervisor's part.…

  4. How to Supervise a Ph.D.

    Science.gov (United States)

    Connell, R. W.

    1985-01-01

    A discussion of the process and problems of supervising a doctoral candidate examines the nature of the task and relationship, the stages of research (defining the topic, design, gathering material, writing, defense, and dissemination), criticism and intellectual growth, and references and sponsorship. (MSE)

  5. Parallelprocesser og deres tilblivelse i supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2006-01-01

    Kapitlet beskæftiger sig med ”Parallelprocesser og deres tilblivelse i supervision”. Først indkredses parallelprocesbegrebet i dets mange variationer. Der er tale om et nøglebegreb i psykoanalytisk supervision, der overordnet set henviser til en relationel positionering eller tematik i...

  6. Exploring Principals' Perceptions of Supervised Agricultural Experience

    Science.gov (United States)

    Rayfield, John; Wilson, Elizabeth

    2009-01-01

    This study explored the perceptions of principals at high schools with agricultural education programs in regard to Supervised Agricultural Experience (SAE). There is evidence that suggests that high school principals' attitudes may both directly and indirectly affect factors that influence school climate and student achievement. In this study,…

  7. A Social Reconstruction Model of Supervision.

    Science.gov (United States)

    Seda, E. Elliott

    This paper presents a social reconstructionist model of supervision. The model connects schools and society, and considers the vital role teachers, students, staff, and others play in developing, designing, and implementing reforms in school and society. The model is based on the philosophy of social reconstructionism, which views schools as…

  8. Experiences of Supervision at Practice Placement Sites

    Directory of Open Access Journals (Sweden)

    Lesley Diack

    2014-01-01

    Full Text Available Background. Whilst placement supervision and clinical education programmes are of significant value in shaping the behaviours of undergraduate healthcare students, appropriate provisions which are efficacious to the learner are somewhat lacking, particularly for students studying on UK MPharm programmes. Objectives. To explore and explain the value of placement supervision to the personal development and employability of undergraduate pharmacy students. Methods. Students participated in a week long community pharmacy pilot programme, a result of a collaborative effort between the School of Pharmacy and Life Sciences and a small consortium of community pharmacies. Students and stakeholders were asked to evaluate their experiences via separate questionnaires which had been developed to elicit views and attitudes. Key Findings. Feedback from students and stakeholders towards the experience was overwhelmingly positive with multiple benefits being reported. Of particular prominence was the emphasis in student feedback on the value of placement supervision to their professional and personal development. Findings were indicative of a development in clinical practice proficiencies, core skills, and improvement in decision-making practice. Conclusions. The benefits of clinical supervision to the professional and personal development of MPharm students are well documented, although attracting professional pharmacy supervisors is proving a problematic task for educational providers in the UK.

  9. Supervised classification of solar features using prior information

    Directory of Open Access Journals (Sweden)

    De Visscher Ruben

    2015-01-01

    Full Text Available Context: The Sun as seen by Extreme Ultraviolet (EUV telescopes exhibits a variety of large-scale structures. Of particular interest for space-weather applications is the extraction of active regions (AR and coronal holes (CH. The next generation of GOES-R satellites will provide continuous monitoring of the solar corona in six EUV bandpasses that are similar to the ones provided by the SDO-AIA EUV telescope since May 2010. Supervised segmentations of EUV images that are consistent with manual segmentations by for example space-weather forecasters help in extracting useful information from the raw data. Aims: We present a supervised segmentation method that is based on the Maximum A Posteriori rule. Our method allows integrating both manually segmented images as well as other type of information. It is applied on SDO-AIA images to segment them into AR, CH, and the remaining Quiet Sun (QS part. Methods: A Bayesian classifier is applied on training masks provided by the user. The noise structure in EUV images is non-trivial, and this suggests the use of a non-parametric kernel density estimator to fit the intensity distribution within each class. Under the Naive Bayes assumption we can add information such as latitude distribution and total coverage of each class in a consistent manner. Those information can be prescribed by an expert or estimated with an Expectation-Maximization algorithm. Results: The segmentation masks are in line with the training masks given as input and show consistency over time. Introduction of additional information besides pixel intensity improves upon the quality of the final segmentation. Conclusions: Such a tool can aid in building automated segmentations that are consistent with some ground truth’ defined by the users.

  10. Childhood unintentional injuries: Supervision and first aid provided

    Directory of Open Access Journals (Sweden)

    Annabel Jonkheijm

    2013-01-01

    Full Text Available Background : The aim of this study was to investigate the circumstances surrounding unintentional injuries of children and the appropriateness of the first aid provided by caregivers. Materials and Methods : This prospective study included children with aged range 0-12 years, who presented with an unintentional injury at the Trauma Unit of a hospital in Cape Town, South Africa, over a 3 month period. Caregivers were interviewed about the circumstances of the injury and the first aid provided. Experts classified the first aid as appropriate, appropriate but incomplete, or inappropriate. Results: A total of 313 children were included with a median age of 3.75 years. The most common causes of injury were falls (39.6%, n = 124, burns (23.9%, n = 75 and motor vehicle crashes (10.5%, n = 33. More than a quarter of the children (27.2%, n = 81 had been left under the supervision of another child below the age of 12. When the injury occurred, 7.1% (n = 22 of the children were unattended. First aid was provided in 43.1% (n = 134 of the cases. More than half of these interventions (53%, n = 72 were inappropriate or appropriate but incomplete. Conclusions: Especially young children are at risk for unintentional injuries. Lack of appropriate supervision increases this risk. Prevention education of parents and children may help to protect children from injuries. First-aid training should also be more accessible to civilians as both the providing of as well as the quality of first-aid provided lacked in the majority of cases.

  11. Building multiclass classifiers for remote homology detection and fold recognition

    Directory of Open Access Journals (Sweden)

    Karypis George

    2006-10-01

    Full Text Available Abstract Background Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems. Results We present a comprehensive evaluation of a number of methods for building SVM-based multiclass classification schemes in the context of the SCOP protein classification. These methods include schemes that directly build an SVM-based multiclass model, schemes that employ a second-level learning approach to combine the predictions generated by a set of binary SVM-based classifiers, and schemes that build and combine binary classifiers for various levels of the SCOP hierarchy beyond those defining the target classes. Conclusion Analyzing the performance achieved by the different approaches on four different datasets we show that most of the proposed multiclass SVM-based classification approaches are quite effective in solving the remote homology prediction and fold recognition problems and that the schemes that use predictions from binary models constructed for ancestral categories within the SCOP hierarchy tend to not only lead to lower error rates but also reduce the number of errors in which a superfamily is assigned to an entirely different fold and a fold is predicted as being from a different SCOP class. Our results also show that the limited size of the training data makes it hard to learn complex second-level models, and that models of moderate complexity lead to consistently better results.

  12. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Supervisor's HEXACO personality traits and subordinate perceptions of abusive supervision

    NARCIS (Netherlands)

    Breevaart, Kimberley; Vries, de Reinout E.

    2017-01-01

    Abusive supervision is detrimental to both subordinates and organizations. Knowledge about individual differences in personality related to abusive supervision may improve personnel selection and potentially reduce the harmful effects of this type of leadership. Using the HEXACO personality framewor

  14. Rules for Supervision and Inspection of Offshore Oil Industry

    Institute of Scientific and Technical Information of China (English)

    Dai Zhongliang; Song Lisong

    1994-01-01

    @@ In short,safety supervision and technique inspection mean the safety supervision by the government,and the inspection by technical organization,and those are put into practice by a series of administrative rules and regulations.

  15. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  16. A Sequential Algorithm for Training Text Classifiers

    CERN Document Server

    Lewis, D D; Lewis, David D.; Gale, William A.

    1994-01-01

    The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.

  17. Development of well construction and workover supervising in Russian Federation

    Science.gov (United States)

    Sizov, A.; Boyarko, G.; Shenderova, I.

    2014-08-01

    Despite long history of drilling supervising it still has a number of uncertainties. The period of rapid rise in supervising development at the beginning of the 90's changed in the 2000's. The necessity in the development of this sphere is obvious. The author describes the history of supervising, period of its market condition adaptation. The research also gives principles methods of supervising development and first steps for its position improvement.

  18. Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

    OpenAIRE

    Jaewook Jung; Leihan Chen; Gunho Sohn; Chao Luo; Jong-Un Won

    2016-01-01

    Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is an essential facility for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning ...

  19. Clients' Views of Live Supervision and Satisfaction with Therapy.

    Science.gov (United States)

    Locke, Lisa D.; McCollum, Eric E.

    2001-01-01

    Examines clients' perceptions of live supervision and their satisfaction with therapy in a university-based training clinic for marriage and family therapists. Results indicate that clients were generally satisfied with therapy and found the live-supervision process satisfactory as long as the perceived helpfulness of live supervision outweighed…

  20. Supervising development in petroleum industry of the Russian Federation

    OpenAIRE

    Sizov, Aleksey Valentinovich; Tretyakov, Konstantin Nikolaevich; Boyarko, Grigory Yurievich; Shenderova, Inna Vladimirovna; Shakirov, A.

    2015-01-01

    Supervising has strongly consolidated in the domestic petroleum services market of the Russian Federation. But despite the rapid growth of the supervising services market, there is a definite need for its further development. In the article, the developments of supervising in petroleum industry of the Russian Federation, as well as possible ways of its improvement are considered.

  1. Does Live Supervision Make a Difference? A Multilevel Analysis

    Science.gov (United States)

    Silverthorn, Brandon C.; Bartle-Haring, Suzanne; Meyer, Kevin; Toviessi, Paula

    2009-01-01

    While the benefit of live supervision on clinical training is largely unquestioned, research that examines how live supervision affects the therapeutic process is lacking. Although marriage and family therapy has embraced this method of supervision, there is little empirical evidence suggesting it "works." This study uses hierarchical linear…

  2. A Delphi Study and Initial Validation of Counselor Supervision Competencies

    Science.gov (United States)

    Neuer Colburn, Anita A.; Grothaus, Tim; Hays, Danica G.; Milliken, Tammi

    2016-01-01

    The authors addressed the lack of supervision training standards for doctoral counseling graduates by developing and validating an initial list of supervision competencies. They used content analysis, Delphi polling, and content validity methods to generate a list, vetted by 2 different panels of supervision experts, of 33 competencies grouped…

  3. Style and Quality in Research Supervision: The Supervisor Dependency Factor.

    Science.gov (United States)

    Kam, Booi Hon

    1997-01-01

    A survey of 250 graduate research students examined the extent of dependency on supervisors in a range of research-related tasks, and how that dependency affected the research supervision process. Results suggest appropriate research supervision has no set prescription, but interactions among quality and style of supervision, role expectations of…

  4. Testing Group Supervision in Fieldwork Training for Social Work Students

    Science.gov (United States)

    Zeira, Anat; Schiff, Miriam

    2010-01-01

    This study monitors group supervision for students' field training in a Bachelor's Degree in Social Work (BSW) program and compares it with the experience of the students receiving the traditional individual supervision. The experimental group supervision model is implemented in two consecutive years. Students' experiences are compared at three…

  5. 18 CFR 367.9110 - Account 911, Supervision.

    Science.gov (United States)

    2010-04-01

    ..., Supervision. 367.9110 Section 367.9110 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT Operation and Maintenance Expense Chart of Accounts § 367.9110 Account 911, Supervision. This account must include the cost of labor and expenses incurred in the general direction and supervision...

  6. 28 CFR 2.95 - Early termination from supervision.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Early termination from supervision. 2.95 Section 2.95 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT... Parolees § 2.95 Early termination from supervision. (a)(1) Upon its own motion or upon request of a...

  7. Testing Group Supervision in Fieldwork Training for Social Work Students

    Science.gov (United States)

    Zeira, Anat; Schiff, Miriam

    2010-01-01

    This study monitors group supervision for students' field training in a Bachelor's Degree in Social Work (BSW) program and compares it with the experience of the students receiving the traditional individual supervision. The experimental group supervision model is implemented in two consecutive years. Students' experiences are compared at three…

  8. 25 CFR 213.43 - Relinquishment of Government supervision.

    Science.gov (United States)

    2010-04-01

    ... 25 Indians 1 2010-04-01 2010-04-01 false Relinquishment of Government supervision. 213.43 Section... Relinquishment of Government supervision. All oil and gas leases hereafter executed shall contain the following relinquishment of supervision clause and terms operative after such relinquishment, or other provisions...

  9. 48 CFR 32.503-2 - Supervision of progress payments.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Supervision of progress... GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING Progress Payments Based on Costs 32.503-2 Supervision of progress payments. (a) The extent of progress payments supervision, by prepayment review...

  10. 34 CFR 303.501 - Supervision and monitoring of programs.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Supervision and monitoring of programs. 303.501 Section... INFANTS AND TODDLERS WITH DISABILITIES State Administration General § 303.501 Supervision and monitoring... supervision of programs and activities receiving assistance under this part; and (2) The monitoring...

  11. 7 CFR 27.73 - Supervision of transfers of cotton.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Supervision of transfers of cotton. 27.73 Section 27... Supervision of transfers of cotton. Whenever the owner of any cotton inspected and sampled for classification... be effected under the supervision of an exchange inspection agency or a supervisor of...

  12. 28 CFR 570.44 - Supervision and restraint requirements.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Supervision and restraint requirements... PROGRAMS AND RELEASE COMMUNITY PROGRAMS Escorted Trips § 570.44 Supervision and restraint requirements. Inmates under escort will be within the constant and immediate visual supervision of escorting staff...

  13. 18 CFR 367.9070 - Account 907, Supervision.

    Science.gov (United States)

    2010-04-01

    ..., Supervision. 367.9070 Section 367.9070 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT Operation and Maintenance Expense Chart of Accounts § 367.9070 Account 907, Supervision. This account must include the cost of labor and expenses incurred in the general direction and supervision...

  14. 9 CFR 590.418 - Supervision of marking and packaging.

    Science.gov (United States)

    2010-01-01

    ... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Supervision of marking and packaging...) Identifying and Marking Product § 590.418 Supervision of marking and packaging. (a) Evidence of label approval... container except by an inspector or under the supervision of an inspector or other person authorized by...

  15. 49 CFR 237.133 - Supervision of repairs and modifications.

    Science.gov (United States)

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Supervision of repairs and modifications. 237.133... Supervision of repairs and modifications. Each repair or modification pursuant to this part shall be performed under the immediate supervision of a railroad bridge supervisor as defined in § 237.55 of this part...

  16. 48 CFR 852.236-78 - Government supervision.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Government supervision. 852.236-78 Section 852.236-78 Federal Acquisition Regulations System DEPARTMENT OF VETERANS AFFAIRS... Government supervision. As prescribed in 836.572, insert the following clause: Government Supervision...

  17. 7 CFR 27.80 - Fees; classification, Micronaire, and supervision.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Fees; classification, Micronaire, and supervision. 27... Classification and Micronaire § 27.80 Fees; classification, Micronaire, and supervision. For services rendered by... classification and Micronaire determination results certified on cotton class certificates.) (e) Supervision,...

  18. 46 CFR 380.23 - Supervision of records.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 8 2010-10-01 2010-10-01 false Supervision of records. 380.23 Section 380.23 Shipping... § 380.23 Supervision of records. (a) Contractors and others subject to the provisions of this subpart... shall be responsible for supervision of its document retention and disposal program. Immediately...

  19. 18 CFR 367.9010 - Account 901, Supervision.

    Science.gov (United States)

    2010-04-01

    ..., Supervision. 367.9010 Section 367.9010 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT Operation and Maintenance Expense Chart of Accounts § 367.9010 Account 901, Supervision. This account must include the cost of labor and expenses incurred in the general direction and supervision...

  20. 46 CFR 131.420 - Manning and supervision.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Manning and supervision. 131.420 Section 131.420 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) OFFSHORE SUPPLY VESSELS OPERATIONS Sufficiency and Supervision of Crew of Survival Craft § 131.420 Manning and supervision. (a) There must...