| A coordinate descent margin based-twin support vector machine for classification. | |
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MedLine Citation:
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PMID: 21890319 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Twin support vector machines (TWSVMs) obtain faster learning speed by solving a pair of smaller SVM-type problems. In order to increase its efficiency further, this paper presents a coordinate descent margin based twin vector machine (CDMTSVM) compared with the original TWSVM. The major advantages of CDMTSVM lie in two aspects: (1) The primal and dual problems are reformulated and improved by adding a regularization term in the primal problems which implies maximizing the "margin" between the proximal hyperplane and bounding hyperplane, yielding the dual problems to be stable positive definite quadratic programming problems. (2) A novel coordinate descent method is proposed for our dual problems which leads to very fast training. As our coordinate descent method handles one data point at a time, it can process very large datasets that need not reside in memory. Our experiments on publicly available datasets indicate that our CDMTSVM is not only fast, but also shows good generalization performance. |
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Authors:
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Yuan-Hai Shao; Nai-Yang Deng |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-8-17 |
Journal Detail:
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Title: Neural networks : the official journal of the International Neural Network Society Volume: - ISSN: 1879-2782 ISO Abbreviation: - Publication Date: 2011 Aug |
Date Detail:
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Created Date: 2011-9-5 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011 Elsevier Ltd. All rights reserved. |
Affiliation:
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Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, PR China. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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