SVM法分类:它的内容和挑战

§ 1　 IntroductionIf you knock the word“SVM”in the SCI index tool on International network,youwould take on thousands of records immediately.This shows its great effects on ourworld.SVM,namely,support vector machines have been successfully applied to a numberof applications ranging from particle identification and text categorization to engine knockdetection,bioinformatics and database marketing1— 6] .The approach is systematic andproperly motivated by statistical learning theory7] .…

SVM classification:Its contents and challenges

SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification,regression or novelty detection.In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically.There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation.In this paper,survey of the key contents on this subject,focusing on the most well-known models based on kernel substitution,namely SVM,as well as the activated fields at present and the development tendency,is presented.