Support Vector Machine Algorithm in Machine Learning

Abstract

The Support Vector methods was proposed by V.Vapnik in 1965, when he was trying to solve problems in pattern recognition. In 1971, Kimeldorf proposed a method of constructing kernel space based on support vectors. In 1990s, V.Vapnik formally introduced the Support Vector Machine (SVM) methods in Statistical Learning. Since then, SVM has been widely applied in pattern recognition, natural language process and so on. Informally, SVM is a binary classifier. The model is based on the linear classifier with the optimal margin in the feature space and thus the learning strategy is to maximize the margin, which can be transformed into a convex quadratic programming problem. It uses the principle of structural risk minimization instead of empirical risk minimization to fit small data samples. Kernel trick is used to transform non-linear sample space into linear space, decreasing the complexity of algorithm. Even though, it still has broader prospects for development.