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- W2359643351 abstract "In order to improve the efficiency of the multi-class classifiers based on support vector machine(SVM),the multi-sphere method was introduced to supervised learning.By training one-class SVM(1-SVM) on the samples class by class,a classifier composed of multiple spheres was obtained.To remove the redundant region in the spheres,a compacted one-vs-rest classifier was used to separate the mixed samples.These two complementary classifiers can be combined into a weighted classifier of one-vs-rest and multi-spheres.The regularization method of the weight factor and other parameters was given based on cross validation.Simulation showed that the novel classifier has higher accuracy with less training time when compared with one-vs-rest classifier,and its decision rate is faster than that of one-vs-one classifier.Consequently,the novel classifier is helpful for solving multi-class problems on large scale systems." @default.
- W2359643351 created "2016-06-24" @default.
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- W2359643351 date "2009-01-01" @default.
- W2359643351 modified "2023-09-28" @default.
- W2359643351 title "Novel one-vs-rest classifier based on SVM and multi-spheres" @default.
- W2359643351 hasPublicationYear "2009" @default.
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