Anovel和快速基于SVM的多类分类器.pdfVIP

Pattern Recognition 39 (2006) 2258–2264 /locate/patcog A novel and quick SVM-based multi-class classifier a,b ,∗ a c Yiguang Liu , Zhisheng You , Liping Cao aInstitute of Image Graphics, School of Computer Science and Engineering, Sichuan University, Chengdu 610064, PR China b Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology, Chengdu 610054, PR China cSichuan University Library, Sichuan University, Chengdu 610064, PR China Received 1 December 2005; received in revised form 25 May 2006; accepted 31 May 2006 Abstract Use different real positive numbers pi to represent all kinds of pattern categories, after mapping the inputted patterns into a special feature space by a non-linear mapping, a linear relation between the mapped patterns and numbers pi is assumed, whose bias and coefficients are undetermined, and the hyper-plane corresponding to zero output of the linear relation is looked as the base hyper-plane. To determine the pending parameters, an objective function is founded aiming to minimize the difference between the outputs of the patterns belonging to a same type and the corresponding pi , and to maximize the distance between any two different hyper-planes corresponding to different pattern types. The objective function is same to that of support vector regression in form, so the coefficients and bias of the linear relation