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- W2392413201 abstract "Hyper-Sphere Multi-Class SVM(HSMC-SVM) is a kind of direct-model multi-class classifiers,and its training speed and testing efficiency are high.However,with the one-order norm soft-margin,classifying precision of HSMC-SVM is affected.In order to improve the classifying precision,least square method was introduced in HSMC-SVM.As a result,a kind of new multi-class classifiers,Least Square Hyper-Sphere Multi-Class SVM(LSHS-MCSVM),was proposed.Simultaneously,the training algorithm and decision rules of LSHS-MCSVM were discussed too.Thus the classifying theory of LSHS-MCSVM was built completely.Shown in the numeric experiments,LSHS-MCSVM exceeds HSMC-SVM in both training speed and classifying precision.Hence,it is suitable for the situations with lots of classification categories and large scale of training samples." @default.
- W2392413201 created "2016-06-24" @default.
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- W2392413201 date "2009-01-01" @default.
- W2392413201 modified "2023-09-23" @default.
- W2392413201 title "Least Square Hyper-sphere Multi-class SVM" @default.
- W2392413201 hasPublicationYear "2009" @default.
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