Support Vector Machines for Multi-Class Pattern Recognition Based on Improved Voting Strategy

摘要：

The improved voting strategy for pairwise classification of multi-class support vector machine (MSVM) is proposed. The new voting strategy can increase recognition accuracy and resolve the unclassifiable region problems caused by conventional pairwise classification. The improved voting value equals to the traditional voting value plus the tuning function. For the data in the classifiable regions, the classification results using improved voting strategy are the same as that using the traditional one. However, the data in the unclassifiable region can be determined by the tuning function. By computer simulations using four UCI data sets, the superiorities of the presented multi-class strategy are demonstrated.

关键词： Multi-class Support Vector Machine (MSVM) Voting Strategy Unclassifiable Region Pairwise Classification

作者: Zhuoda Jiang

作者单位: Key Laboratory of Numerical Control of Jiangxi Province, Jiujiang University, Jiujiang, Jiangxi, 332005, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 517-520

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）