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《Journal of System Simulation》 2008-24
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A Kind of Fast Fuzzy Support Vector Machines

LIU Hong-bing1,2, XIONG Sheng-wu1 (1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China; 2. Department of Computer Science, Xinyang Normal University, Xinyang 464000, China)  
The two kinds of Fuzzy Support Vector Machines (FSVMs) which respectively were proposed by H. P. Huang, C. F. Lin, etc. and T. Inoue, S. Abe, etc. were improved Support Vector Machines (SVMs). They respectively solved the overfitting problem and reduced the unclassifiable regions of multi-class problems. It is urgent for SVMs to deal with outliers properly and speed up training for the training set of large scale. Regarding to the requirements, a kind of fast FSVMs integrating the advantages of above FSVMs was proposed. During the training process, the membership functions were defined by the distance between the data and their class centers and assign the lager penalty values for the data which are easy to be misclassified. The selected edge data including outliers with the lager membership values were used training FSVMs. During the testing process, the classes of the unknown data were discriminated by the 1-against-1 strategy and decision functions of FSVMs. The two-class problem including outliers and the multi-class problem, such as hand-written digit recognition in the machine learning benchmark dataset, have verified the reduced training time and the improved generalization abilities of the proposed fast FSVMs.
【Fund】: 国家自然科学基金(40701153 60572015);; 武汉市国际交流项目(200770834318);; 信阳师范学院青年骨干教师资助计划(2060503)
【CateGory Index】: TP181
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