Circuits, Communications and Systems, Pacific-Asia Conference on (2009)
May 16, 2009 to May 17, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACCS.2009.64
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.
support vector machine (SVM); multi-class SVM; SMO algorithm; work set selection; LSHS-MCSVM
T. Xu, "A New Sphere-Structure Multi-Class Classifier," 2009 Pacific-Asia Conference on Circuits, Communications and Systems (PACCS 2009)(PACCS), Chengdu, 2009, pp. 520-525.