AN ON-LINE LEARNING APPROACH WITH SUPPORT VECTOR DORMAIN CLASSIFIER

摘要：

As one kind of one-class classifier, support vector domain classifier (SVDC) has worked well for the batch model learning problems. But with real-world database increase in size, there is a need to scale up inductive learning algorithm to handle more training data. On-line learning technique is one possible solution to the scalability problem, where data is processed in parts, and the result combined so as to use less memory. This paper presented an on-line learning algorithm based on SVDC, and the basic idea of the proposed algorithm is to obtain the initial target concepts using SVDC during the training phase and then update these target concepts by an updating model. Different from the existed on-line learning approaches, in our algorithm, the model updating procedure equals to solve a quadratic programming (QP) problem, and the updated model still owns the property of spars solution.Compared with other existed on-line learning algorithms, the inverse procedure of our algorithm (i.e. decreasing learning) is easy to conduct without extra computation.

关键词： Support Vector Domain Classifier On-line learning Classification

作者: YING-GANG ZHAO SHUO-PING WANG YANG-GUANG LIU QIN-MING HE

作者单位: College of Computer Science, Zhejiang University, Hangzhou 310027, China School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China School of Information Science & Engineering, Zhejiang University, Institute of Technology, Ningbo 31 College of Computer Science, Zhejiang University, Hangzhou 310027, China;School of Information Scien

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3600-3604

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）