A New Sphere-Structure Multi-Class Classifier

Hyper-Sphere Multi-Class SVM (HSMC-SVM) is a kind of direct-model multi-class classifiers, and its training and testing speed are high. However, with the one-order norm soft-margin, classifying precision of HSMC-SVM is affected. In order to improve the classifying precision, least square method is introduced in HSMC-SVM. As a result, a kind of new multi-class classifiers, Least Square Hyper-Sphere Multi-Class SVM (LSHS-MCSVM), is proposed. Simultaneously, the training algorithm and decision rules of LSHS-MCSVM are discussed too. Thus the classifying theory of LSHS-MCSVM is built completely. Shown in the numeric experiments, LSHS-MCSVM excels HSMC-SVM at both training speed and classifying precision. Hence, it is suitable for the situations with lots of classification categories and large scale of training samples.

[1]  Friedhelm Schwenker,et al.  Hierarchical support vector machines for multi-class pattern recognition , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[2]  V. Balakrishnan,et al.  Branch and bound algorithm for global optimization in control theory , 1993 .

[3]  Chen Mianyun.  On Multiclass Classification Methods for Support Vector Machines , 2005 .

[4]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[5]  Dake He,et al.  A New Orientation for Multi-Class SVM , 2007 .

[6]  Jean Cea,et al.  Optimization - Theory and Algorithms , 1978 .

[7]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[8]  S. Abe,et al.  Decision-tree-based multiclass support vector machines , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[9]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[11]  Robert P. W. Duin,et al.  Data domain description using support vectors , 1999, ESANN.

[12]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .