Identifying knowledge domain and incremental new class learning in SVM

Abstract

An incremental class learning system for support vector machine (SVM) is presented for learning new knowledge from newly available data without forgetting the existing knowledge. We present algorithms for knowledge domain description, new knowledge detection, and incremental learning of new class knowledge. We have applied the incremental learning system to a data set provided by the UCI machine learning Web site, and the results show that the proposed SVM incremental class learning system is quite effective.