The Study And Application Of Support Vector Machine Multiclass Classification

Support vector machine is a new machine learning technique developed from the middle of 1990s by Vladimir Vapnik. Support vector machines are a very specific class of algorithms, characterized by the use of a maximal margin hyper-plane the theory of kernels, the absence of local minima, convex optimization the sparseness of the solution, Mercer's theorem and the capacity control obtained by acting on the margin. A large number of experiments have shown that support vector machine has not only simpler structure, but also better performance, especially its better generalization ability. The support vector machines approach was originally developed to solve binary classification problems. But in many fields, we need to solve multi-class classification problems. How to effectively extend it for multi-class classification is still an on-going research issue.In this paper, some problems of support vector machines algorithms are analyzed. 1) An overview on a variety of classification algorithms for support vector machines is given. We have carried on in-depth analysis to the many kinds of support vector machines algorithms that existed at present, and compared their performance and range of application. 2) SVM for multi-class problems is discussed. Several methods have been proposed including "one-against all", "one-against-one", DAGSVM, Classification method of multi-class SVM based on binary tree, and so on. And their pluses and minuses and performances are compared. 3) Furthermore, hyper-sphere structured SVMs are discussed, and we provide an algorithm called hyper-sphere SVMs based on binary tree. We show the algorithm in detail and analyze its characteristics.