Adaptive Support Vector Classifications

摘要

Support Vector machines (SVM) was originally designed for regression and binary classification. It promises to give good generalization and has been applied to various tasks. The basic idea behind SVM is to do the classification through solving a nonlinear(quadratic) programming. In this paper, we concentrate on adaptive support vector classification problems. Since there are many parameters in the kernel functions of SVM, tuning the smooth parameters can certainly improve the performance of classification. The general literature of SVM has not discussed in detail the subject of tuning the various user defined parameters. In this paper, we explore the trade-off between maximum margin and classification errors and estimate the best kernel parameters. Toy and real life data are used in the experiments.