Pattern recognition (psychology)

Structural risk minimization

Statistical learning theory

Mathematics

Quadratic classifier

Binary classification

Radial basis function kernel

Margin (machine learning)

Support vector machine

Empirical risk minimization

Mathematical optimization

Linear classifier

Machine learning

Kernel method

Structured support vector machine

Polynomial kernel

Margin classifier

Relevance vector machine

Artificial intelligence

Computer science

Quadratic programming

Feature vector

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.

Support Vector Machine Algorithm in Machine Learning