Title :
A New Fuzzy Multicategory Support Vector Machines Classifier
Author :
Lu, Shu-xia ; Liu, Xian-Hao ; Zhai, Jun-hai
Abstract :
This paper proposes a new fuzzy multicategory support vector machines (FMSVM) classifier. The main idea is that the proposed FMSVM uses knowledge of the ambiguity associated with the membership of samples for a given class and the relative location of samples to the origin. Compared with the existing SVMs, the new proposed FMSVM that uses the L2-norm in the objective function has the improvement in aspects of classification accuracy and reducing the effects of noises and outliers.
Keywords :
fuzzy set theory; pattern classification; support vector machines; L2-norm; fuzzy multicategory classifier; support vector machines; Computer science; Cybernetics; Electronic mail; Fuzzy sets; Machine learning; Mathematics; Noise reduction; Research and development; Support vector machine classification; Support vector machines; Fuzzy membership; Multicategory classification; Support vector machines;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370635
