Improved Fuzzy Multicategory Support Vector Machines Classifier

Author

Wang, Xi-Zhao ; Lu, Shu-xia

Author_Institution

Machine Learning Center, Hebei Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3585

Lastpage

3589

Abstract

This paper investigates an improved fuzzy multicategory support vector machines classifier (IFMSVM). It uses knowledge of the ambiguity associated with the membership of data samples of a given class and relative location to the origin, to improve classification performance with high generalization capability. In some aspects, classifying accuracy of the new algorithm is better than that of the classical support vector classification algorithms. Numerical simulations show the feasibility and effectiveness of this algorithm

Keywords

computational complexity; fuzzy set theory; optimisation; pattern classification; support vector machines; fuzzy multicategory support vector machine classifier; support vector classification algorithm; Classification algorithms; Computer science; Cybernetics; Electronic mail; Machine learning; Mathematics; Numerical simulation; Quadratic programming; Support vector machine classification; Support vector machines; Testing; Fuzzy membership; Multicategory data classification; Quadratic programming; Support vector machines (SVMs);

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258575

Filename

4028692