文献类型：期刊文献

英文题名：Multi-class learning with specific features for pairwise classes

作者：Yan, Jianjun[1]; Shen, Qingwei[1]; Zhou, Chiheng[1]; Ren, Jintao[1]; Guo, Rui[2]

机构：[1] Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] Center for TCM Information Science and Technology, Shanghai University of TCM, Shanghai 201203, China

年份：2011

卷号：4

起止页码：2054

外文期刊名：Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

收录：EI(收录号：20120314690832)

语种：英文

外文关键词：Classification (of information)

摘要：Support vector machine is initially developed for binary classification problem. Multiclass support vector machine (MSVM) is usually realized by using a combination of several binary SVMs. In most of the existing MSVM approaches, all binary SVMs operates on the same feature space. This paper proposed a new approach in which each binary SVM is associated with a specific feature representation. Based on the idea, we developed an algorithm for MSVM named REAL. In the experiment its performance is compared with traditional approaches on 17 real-world multi-class datasets. The good performance achieved by the algorithm clearly verifies the effectiveness of this approach. ? 2011 IEEE.