Theory of hypersphere multiclass SVM
超球体多类支持向量机理论

Constructed by standard binary classes support vector machine(SVM), present multiclass SVMs are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very difficult. By extending the hypersphere one-class SVM(HSOC-SVM) to a hypersphere multiclass SVM(HSMC-SVM), we build a fast training classifier HSOC-SVM. Its training speed is higher than that of the present multiclass classifiers, because each category data trains only one HSOC-SVM. In order to improve the training speed for the HSMC-SVM, we propose a training algorithm based on the existing algorithm for SMO. Meanwhile, the theoretic upper bound of the generalized error of HSMC-SVM is analyzed for evaluating the general performance of HSMC-SVM. Numeric experiments show that the training speed of HSMC-SVM is especially improved when many categories of data are to be classified. Thus, HSMC-SVM provides a new idea for developing fast-directed multiclass classifiers in machine learning area.