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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/4658

Title: A Bottom-Up Method for Simplifying Support Vector Solutions
Authors: Nguyen, DucDung
Ho, Tu Bao
Keywords: Feature space
input space
kernel methods
reduced set method
support vector machines (SVMs)
Issue Date: 2006-05
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE Transactions on Neural Networks
Volume: 17
Number: 3
Start page: 792
End page: 796
DOI: 10.1109/TNN.2006.873287
Abstract: 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
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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