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《Journal of Xiamen University(Natural Science)》 2010-01
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Analysis of Multi-classification Based on SVM in Different Feature Space

ZHOU Qi-feng,HONG Wei-cai,SHAO Gui-fang (School of Information Science and Technology,Xiamen University,Xiamen 361005,China)  
There usually using the same kernel parameter in multi-classification methods based on SVM such as one versus one(1-v- 1)and one versus rest(1-v-r).Optimizing each sub-classifier under different kernel parameter is equal to classifying in different feature space.Then the comparability of each sub-classifier's output should be analyzed before using the MaxWin strategy directly.In this paper, a relative margin is introduced to analyse the outputs of sub-classifiers.Results show that using different kernel parameters to optimizing each sub-classifier respectively,the outputs of decision function are still comparablk,and furthermore,it performs the better generalization ability.
【Fund】: 福建省自然科学基金(2009J05153)
【CateGory Index】: TP18
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