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)

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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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