Abstract
Support vector machines (SVMs) are powerful tools for providing solutions to classification and function approximation problems. The comparison among the four classification methods is conducted. The four methods are Lagrangian support vector machine (LSVM), finite Newton Lagrangian support vector machine (NLSVM), smooth support vector machine (SSVM) and finite Newton support vector machine (NSVM). The comparison of their algorithm in generating a linear or nonlinear kernel classifier, accuracy and computational complexity is also given. The study provides some guidelines for choosing an appropriate one from four SVM classification methods in a classification problem.
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