Title :
Improved Fuzzy Multicategory Support Vector Machines Classifier
Author :
Wang, Xi-Zhao ; Lu, Shu-xia
Author_Institution :
Machine Learning Center, Hebei Univ., Baoding
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);
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258575
