NDLI: The Research on the Algorithm of Approximately Linearly Dividable Support Vector Classification Machine Based on Fuzzy Theory

Content Provider	IEEE Xplore Digital Library
Author	Ai-min Wang Zhimin Yang Wenying Ge
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. & Inf. Eng, Anyang Normal Univ., Anyang (Ai-min Wang)
Abstract	Data mining is a new filed in data processing research. Support vector machine (SVM) is a useful method adopted in data mining. However, when the training set of the SVM contains information of uncertainty, the SVM can do nothing about it. In order to solve the problem presented above, this article discusses an algorithm of approximately linearly dividable support vector classification machine based on fuzzy theory. With the restriction of the confidence lambda(0
Starting Page	213
Ending Page	218
File Size	283130
Page Count	6
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.660
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Uncertainty Linear programming Data processing Data mining approximately linearly dividable Support vector machines Fuzzy sets fuzzy support vector machine constraining programming of uncertain chance Linear approximation Support vector machine classification clearly equivalent programming Fuzzy systems algorithm
Content Type	Text
Resource Type	Article

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The Research on the Algorithm of Approximately Linearly Dividable Support Vector Classification Machine Based on Fuzzy Theory