Improved Fuzzy Multicategory Support Vector Machines Classifier
Xizhao Wang, Shuxia Lu · 2006
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