NDLI: A new nu-support vector machine for training sets with duplicate samples

Content Provider	IEEE Xplore Digital Library
Author	Yin-Shan Jia Chuan-Ying Jia Hong-Wei Qi
Copyright Year	2005
Description	Author affiliation: Sch. of Inf. Technol., Liaoning Univ. of Pet. & Chemicals, Fushun, China (Yin-Shan Jia)
Abstract	Analyzed theoretically, /spl nu/-SVM was found to be over-dependent on each training sample, even if the samples have same value. This dependence would result in more time for training, more support vectors and more decision time. In order to overcome this problem, we propose a new /spl nu/-SVM. This new /spl nu/-SVM multiplies each slack variable in the objective function by a weight factor, and automatically computes each weight factor by the number of corresponding samples with same value before training. Theoretical analysis and the results of experiments show that the new /spl nu/-SVM has the same classification precision rate as the standard /spl nu/-SVM and the new /spl nu/-SVM is faster than the /spl nu/-SVM in training and decision if the training sets have same value samples.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4370
Ending Page	4373
File Size	331079
Page Count	4
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
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 Machine learning Chemical analysis Information technology Petroleum Chemical technology Information analysis Character recognition Speech recognition machine learning duplicate samples weighted support vector machines
Content Type	Text
Resource Type	Article

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