Parameter optimization of SVM -- how to improve the performance of classifier better
2016-08-23
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Used cross validation of thought can in a species meaning Xia get optimal of parameter, can effective of avoid had learning and owes learning State of occurred, eventually for test collection of forecast get more ideal of accurate rate. used instance validation showed that, with cross validation selected out of parameter to training SVM get of model than random of selected parameter training SVM get of model in last classification forecast Shang more effective.
matlab
分类
svm
性能
参数
优化
更好
如何
提升
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