关键词: 增量学习, 边界矢量, 计数器, 支持向量机

Abstract: This paper presents a novel approach to incremental support vector machine (SVM) learning algorithm. It analyses the possible change of support vector set after new samples are added to training set. Furthermore，the active properties of SV set in incremental learning are analyzed. After that，a new improved incremental SVM learning algorithm is proposed，which is based on a counter sifting method. The theoretical analysis and experimental results show that this algorithm could not only improve the training speed，but also reduce storage cost.

Key words: incremental learning, margin vector, counter, support vector machine