The Analysis And Design For The Algorithms Of Multiclass Classification Based On SVM

Support vector machine (SVM) is a statistic learning method based on less samples proposed in recent years. Support vector machine is very specific class of algorithms characterized by the use of the maximal margin hyper-plane the theory of kernels, the absence of local minima, the 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 mufti-class problems is discussed. Several methods have been proposed including "one-against-all", "one-against-one", DAGSVM, Classification method of mufti-class SVM based on binary tree, and so on. And their pluses and minuses and performances are compared. To overcome the support vector machine based on 1-a-r (one–against-rest) method have the accuracy influence of sample imbalance and existing reject area a new support vector machine based on boundary nearest methods was proposed, and acquire very good results. 3) In this part GEPSVM was discussed. First the produce of GEPSVM was introduced. To overcome the result in some case is bad, a new proximal support vector machine based on primary and secondary prototypal hyperplanes is proposed. First, produce the primary prototypal hyperplanes by GEPSVM, than, produce the secondary prototypal hyperplanes using primary prototypal hyperplanes and the information of other class. This method not only simple and easy to realization, but also improves the classification accuracy of GEPSVM, which has been validated on real UCI datasets.4) Proximal support vector machine based on primary and secondary prototypal hyperplanes combining with PCA is applied in face recognition. Compared with traditional classification method, this method improves the classification accuracy.