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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/4658

タイトル: A Bottom-Up Method for Simplifying Support Vector Solutions
著者: Nguyen, DucDung
Ho, Tu Bao
キーワード: Feature space
input space
kernel methods
reduced set method
support vector machines (SVMs)
発行日: 2006-05
出版者: Institute of Electrical and Electronics Engineers (IEEE)
誌名: IEEE Transactions on Neural Networks
巻: 17
号: 3
開始ページ: 792
終了ページ: 796
DOI: 10.1109/TNN.2006.873287
抄録: The high generalization ability of support vector machines (SVMs) has been shown in many practical applications, however, they are considerably slower in test phase than other learning approaches due to the possibly big number of support vectors comprised in their solution. In this letter, we describe a method to reduce such number of support vectors. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed one. Through the analysis of relation between vectors in input and feature spaces, we present the construction of the new vectors that requires to find the unique maximum point of a one-variable function on (0,1), not to minimize a function of many variables with local minima in previous reduced set methods. Experimental results on real life dataset show that the proposed method is effective in reducing number of support vectors and preserving machine's generalization performance.
Rights: Copyright (c)2006 IEEE. Reprinted from IEEE Transactions on Neural Networks, 17(3), 2006, 792-796. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/4658
資料タイプ: publisher
出現コレクション:a10-1. 雑誌掲載論文 (Journal Articles)

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