Construct Offline and Online Membership Functions Based on SVM

摘要：

The classification algorithm presented in this paper consists of Offline and Online Membership Functions, named as OOMF. They cooperated with each other to provide qualified class label of confidence. The offline membership function is derived from decision functions yielded by a weighted SVMs approach (WSVM). The online membership function works in the scenario where offline membership function is of low discrimination. And it is designed by a new kNN (NkNN) that is encoded with a class-wise metric.Some strategies bring computational ease: hyper parameters concerned are tuned context-dependently;training dataset is reduced by a tuning support vector clustering (TSVC); and working set of NkNN is pre-specified. We describe experimental evidence of classification performance improved by our schema over state of the arts on real datasets.

关键词： Offline and online membership function SVM Weighted schema Parameter tuning

作者: Xiangsheng Rong Ping Ling Ming Xu

作者单位: Xuzhou Air Force College of P. L. A, Xuzhou 221000, P. R. China School of Computer Science, Xuzhou Normal University, Xuzhou 221116, P. R. China

会议类型: 国际会议

会议名称: The 2007 International Conference on Intelligent Systems and Knowledge Engineering(第二届智能系统与知识工程国际会议)

会议地点: 成都

会议语种:英文

页码: 864-868

在线出版日期: 2007-10-15（万方平台首次上网日期，不代表论文的发表时间）