NDLI: Radius margin bounds for support vector machines with the RBF kernel

Content Provider	IEEE Xplore Digital Library
Author	Kai-Min Chung Wei-Chun Kao Sun, T. Li-Lun Wang Chih-Jen Lin
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci., Nat. Taiwan Univ., Taipei, Taiwan (Kai-Min Chung; Wei-Chun Kao; Sun, T.; Li-Lun Wang; Chih-Jen Lin)
Abstract	An important approach for efficient support vector machine (SVM) model selection is to use differentiable bounds of the leave-one-out (LOO) error. Past efforts focused on finding tight bounds of LOO. However, their practical viability is still not very satisfactory. Duan et al. (2002) has been shown that radius margin bound gives good prediction for L2-SVM. In this paper, through the analyses why this bound performs well for L2-SVM, we show that finding a bound whose minima are in a region with small LOO values may be more important than its tightness. Based on this principle we propose modified radius margin bounds for L1-SVM where the original bound is only applicable to the hard-margin case. Our modification for L1-SVM achieves comparable performance to L2-SVM.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	893
Ending Page	897
File Size	530969
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1198190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Support vector machines Kernel Support vector machine classification Sun Computer science Computer errors Performance analysis Testing Estimation error Time of arrival estimation
Content Type	Text
Resource Type	Article

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