Abstract
Abstract Classification on high dimensional data arises in many statistical and data mining studies. Support vector machines (SVM) are one of data mining technique which has been extensively studied and have shown remarkable success in many applications. Many researches developed SVM to increase performance such as smooth support vector machine (SSVM). In this study variants of SSVM (spline SSVM, piecewise polynomial SSVM) are proposed for high-dimensional classification. Theoretical results demonstrate piecewise polynomial SSVM has better classification. And numerical comparison results show that the piecewise polynomial SSVM slightly better performance than spline SSVM.
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