Proposition Of A Classification System "Beta - Ls - Sv M" And Its Application To Medical Data Sets
2014 6TH INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR)(2014)
摘要
We apply two techniques of classification, Least Squares Support Vector Machines (LS-SVM) and Sequential Minimum Optimization SVM (SMO-SVM) to some diseases: cancer, hepatitis, heart, thyroid, and diabetes, described in Benchmark data sets. To compare between these techniques, some kernel functions are used which are polynomial, linear, sigmoidal, Gaussian and beta. Therefore the classifier beta - LS - SV M is selected according to its best results.
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关键词
Classification, medical data sets, Support Vector Machines (SVM), Kernel function, LS-SVM, SMO-SVM
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