An Incremental Learning Algorithm Based on Support Vector Domain Classifier

摘要：

Incremental learning technique is usually used to solve large-scale problem. We firstly gave a modified support vector machine (SVM) classification method--support vector domain classifier (SVVC), then an incremental learning algorithm based on SVDC was proposed. The basic idea of this incremental algorithm is to obtain the initial target concepts using SVDC during the training procedure and then update these target concepts by an updating model. Different from the existed incremental 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 incremental learning algorithms, the inverse procedure of our algorithm (i.e. decreasing learning) is easy to conduct without extra computation. Experiment results show our algorithm is effective and feasible.

关键词： Support Vector Machines Support Vector Domain Classifier Incremental learning Classification.

作者: Yinggang Zhao Qinming He

作者单位: College of Computer Science, Zhejiang University, Hangzhou 320027, China

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 805-809

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）