A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2001. Home/Search Document Details and Download
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....Finally, some concluding remarks are given. 2 Kernel Methods We distinguish two components of kernel methods, the kernel machine and the kernel function. Di#erent kernel machines tackle di#erent learning tasks, e.g. support vector machines for supervised learning, support vector clustering [1] for unsupervised learning, and kernel principal component analysis [20] for feature extraction. Thus the kernel machine also implements the search strategy ; however, the hypothesis language can later be adapted by plugging in a di#erent kernel function. Thus the kernel function encapsulates ....
A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, Dec. 2001.
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....In addition to standard methods of pattern recognition, such as the k means algorithm and the hierarchical PCA, two original kernel methods are implemented. The rst one is an extension of Kohonen s Self Organizing Maps (SOM) 4] the second one an extension of Support Vector Clustering (SVC) [1]. The speci city of both extensions rests in the nature of the kernel (the measure of similarity) which takes into account the sequential nature of data. All methods produce almost identical results, namely a classi cation structure made up of two clusters. The consensus sequences associated ....
A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001.
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....Estivill Castro , and Stephan K. Chalup School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW 2308, Australia. ABSTRACT Support Vector Machines (SVMs) have been widely adopted for classification, regression and novelty detection. Recent studies [1, 2] proposed to employ them for cluster analysis too. The basis of this support vector clustering (SVC) is density estimation through SVM training. SVC is a boundarybased clustering method, where the support information is used to construct cluster boundaries. Despite its ability to deal with ....
....of noise. In addition to their accuracy, a key characteristic of SVMs is their mathematical tractability and geometric interpretation. While SVMs have been widely adopted as supervised learning methods with labeled data, they have also been used for the exploration of unlabeled data (cf. [1, 8, 9]) Novelty detection and cluster analysis using SVMs are examples for learning unlabeled data. For many real world problems, the task is not to classify but to detect novel or Current address: School of Computing and Information Technology, Griffith University, Brisbane, QLD 4111, Australia. ....
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A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, 2001.
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....to as kernel methods, to perform various data mining algorithm from the knowledge of the kernel matrix only. Apart from the most famous support vector machine algorithm for classi cation and regression [BGV92, Vap98] other kernel methods include principal component analysis [SSM99] clustering [BHHSV01] Fisher discriminants [MRW 99] or independent component analysis [BJ01] These recent developments open the door to new analysis opportunities which we believe can be particularly suited to the new discipline of proteomics whose central concepts, genes or proteins, are de ned through a ....
Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001.
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Ben-Hur, A., Horn, D., Siegelmann, H. and Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research, 2, 125--137.
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A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2001.
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A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, 2001.
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A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, 2002.
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Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, 2002.
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A. Ben-Hur, D. Horn, H. T. Siegelmann and V. Vapnik. Support vector clustering. In Journal of Machine Learning. revised version Jan 2002.
Computing Core-Sets and Approximate Smallest Enclosing.. - Kumar, Mitchell.. (Correct)
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A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. In Journal of Machine Learning. revised version Jan 2002.
Unsupervised and Semi-supervised Clustering: a Brief Survey - Grira, Crucianu, Boujemaa (2004) (Correct)
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Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125--137, 2002.
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A. Ben-Hur, D. Horn, H. Siegelmann, and V. Vapnik, "Support vector clustering," J. of Machine Learning Research, vol. 2, pp. 125--137, 2001.
Hand Recognition Using Geometric Classifiers - Yaroslav Bulatov Sachin (Correct)
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Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik. Support vector clustering. revised version Jan 2002.
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A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, "Support Vector Clustering," Journal of Machine Learning Research, vol. 2, pp. 125--137, 2001.
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