NDLI: A new algorithm of fuzzy support vector machine based on niche

Content Provider	IEEE Xplore Digital Library
Author	Ying Huang Wei Li
Copyright Year	2010
Description	Author affiliation: School of Mathematics and Computer Gannan, Normal University, Jiangxi , China (Ying Huang) \|\| School of Information Engineering Jiangxi, University of Science and Technology, China (Wei Li)
Abstract	A new algorithm of fuzzy support vector machine based on niche is presented in this paper. In this algorithm, through comparing samples niche with class niche, the method of simply using Euclidean distance to measure the relationship of samples and class in the traditional support vector machine is changed by using the minimum radius in class niche, and the disadvantages of traditional support vector machine, which are sensitive to noise and outliers, and poor performance of differentiation of valid samples are overcome. Experimental data show that compared with the traditional support vector machine which only uses the distance between the sample and the center of class, this new algorithm can improve the convergence speed, and thus greatly enhance the discrimination between valid samples and noise samples.
Starting Page	1
Ending Page	4
File Size	644429
Page Count	4
File Format	PDF
ISBN	9781424468966
e-ISBN	9781424468997
DOI	10.1109/NLPKE.2010.5587796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Graphics support vector machine noise Educational institutions Genetics Size measurement niche membership Data mining Noise measurement
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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