Support vector machine and kernel function characteristic analysis in pattern recognition

Support vector machine (SVM) is a method of machine learning developed in the middle period in 1990s. The difference between SVM and neural network is that the former is based on structure risk minimization principle in pattern recognition, and the latter is based on experience risk minimization principle in pattern recognition. SVM has not only simpler structure, but also better performance, especially better generalization ability. The classification principle of SVM is discussed, and three kinds of kernel function are applied to SVM, which are multinomial function, radial basis function, perceptron function. Finally comparison of three kernel function characteristic is presented by three recognition examples: classification of point set in plane, recognition of similar script Chinese characters, and recognition of double screw curve. The rules of kernel function selection in different recognition matter are presented.