Title :
An Improved Fuzzy Support Vector Machine
Author :
Xiao, Xiaoling ; Zhang, Xiang
Author_Institution :
Sch. of Comput. Sci., Yangtze Univ., Jingzhou, China
Abstract :
Most fuzzy support machines (FSVM) canpsilat effectively distinguish between valid samples and outliers or noises. A fuzzy support vector machine based on the fuzzy connectedness is improved in this paper. The fuzzy connectedness proposed for the measurement of the inaccuracy of samples effectively distinguishes between valid samples and outliers or noises. In the FSVM method, a membership function is defined according to not only the relation between a sample and its class center, but also those among samples, which is described by the fuzzy connectedness among samples. Experimental results show that this improved method is more robust than the traditional SVM, and other FSVM methods.
Keywords :
fuzzy set theory; support vector machines; FSVM method; fuzzy support vector machine; membership function; Computer science; Computer science education; Machine intelligence; Machine learning; Noise measurement; Noise reduction; Noise robustness; Risk management; Support vector machines; Ubiquitous computing; fuzzy connectedness; fuzzy support vector machine; outliers;
Conference_Titel :
Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3619-4
DOI :
10.1109/IUCE.2009.101
