Multi-class Learning with Specific Features for Pairwise Classes
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.
multi-class support vector machine multi-catagory classification MSVM
Jianjun Yan Qingwei Shen Chiheng Zhou Jintao Ren Rui Guo
Center for Mechatronics Engineering East China University of Science and Technology Shanghai 200237, Center for TCM Information Science and Technology Shanghai University of TCM Shanghai 201203, China
国际会议
上海
英文
2063-2066
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）