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Author Hamasuna, Y. ♦ Endo, Y. ♦ Miyamoto, S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Support vector machines ♦ Training data ♦ Classification algorithms ♦ Optimization ♦ Support vector machine classification ♦ Clustering algorithms ♦ Vectors
Abstract This paper presents two new types of support vector machine (SVM) algorithms, one is based on Hard-margin SVM and the other is based on Soft-margin SVM. These algorithms can handle data with tolerance of which the concept includes some errors, ranges or missing values in data. First, the concept of tolerance is introduced into optimization problems of Support Vector Machine. Second, the optimization problems with the tolerance are solved by using the Karush-Kuhn-Tucker conditions. Next, new algorithms are constructed based on the unique and explicit optimal solutions of the optimization problem. Finally, the effectiveness of the proposed algorithms is verified through some numerical examples for the artificial data.
Description Author affiliation: Doctor's Program of Syst. & Inf. Eng., Univ. of Tsukuba, Tsukuba (Hamasuna, Y.)
ISBN 9781424418183
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-01
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 127.84 kB
Page Count 6
Starting Page 750
Ending Page 755


Source: IEEE Xplore Digital Library