文献类型：期刊文献

英文题名：Improved multi-kernel classification machine with Nystrom approximation technique

作者：Zhu, Changming[1];Gao, Daqi[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2015

卷号：48

期号：4

起止页码：1490

外文期刊名：PATTERN RECOGNITION

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000348880300038)】；

基金：This work was partially supported by Natural Science Foundations of China under Grant nos. 61272198 and 21176077, Innovation Program of Shanghai Municipal Education Commission under Grant no. 14ZZ054, the Fundamental Research Funds for the Central Universities, Shanghai Key Laboratory of Intelligent Information Processing of China under Grant no. IIPL-2012-003, Soochow University Jiangsu Provincial Key Laboratory for Computer Information Processing Technology, and Nature Science Foundation of Shanghai Province of China under Grant no. 11ZR1409600.

语种：英文

外文关键词：Multiple kernel learning; Nystrom approximation; Generalization risk analysis; Pattern classification

摘要：Kernelized modification of Ho-Kashyap algorithm with squared approximation of the misclassification errors (KMHKS) is an effective algorithm for nonlinearly separable classification problems. While KMHKS only adopts one kernel function. So a multi-kernel classification machine with reduced complexity named Nystrom approximation matrix with Multiple KMHKSs (NMKMHKS) has been developed. But NMKMHKS has to initialize many parameters and has not an ability to deal with noise well. To this end, we propose an improved multi-kernel classification machine with Nystrom approximation technique (INMKMHKS). INMKMHKS is based on a new way of generating kernel functions and a new Nystrom approximation technique. The contributions of INMKMHKS are that (1) avoiding the problem of setting too many parameters; (2) keeping comparable space and computational complexities after comparing with NMKMHKS; (3) having a tighter generalization risk bound in terms of Rademacher complexity analysis; (4) having a better recognition than NMKMHKS on average; (5) possessing an ability to deal with noise and practical images. (C) 2014 Elsevier Ltd. All rights reserved.