NDLI: A gradual combining method for multi-SVM classifiers based on distance estimation

Content Provider	IEEE Xplore Digital Library
Author	Ying Yu Xiao-Long Wang Bing-Quan Liu
Copyright Year	2004
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., China (Ying Yu; Xiao-Long Wang; Bing-Quan Liu)
Abstract	A fusion algorithm based on multi SVM classifiers is presented in order to improve the performance of SVMs (support vector machines). Different SVM classifiers are trained with special instances. A gradual method based on distance estimation is utilized to combine different SVM classifiers into a sole learner. Instances that are easy to be categorized mistakenly by present classifier will be handed to the next classifier. These instances are chosen according to their distance to the optimal discrimination hyperplane. Evaluation on efficacy of the proposed multi-SVM classifier is carried on Chinese personal name recognition. Experiments show this multi SVM classifiers achieve better performance than that of single SVM learner and SVM ensemble using weighted voting scheme.
Starting Page	3434
Ending Page	3438
File Size	318351
Page Count	5
File Format	PDF
ISBN	0780384032
DOI	10.1109/ICMLC.2004.1380380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Boosting Voting Kernel Handwriting recognition Machine learning Computer science Electronic mail Pattern recognition
Content Type	Text
Resource Type	Article

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