关键词: 支持向量机（SVM）, 增量学习, 数据分布, 超平面距离

Abstract: SVM algorithm is widely used to deal with classification problem due to its good robustness and performance on small datasets. However, the traditional SVM algorithm fails to address some classification problems when the data are large or growing. One of the strategies to overcome this challenge is to train the classifier using an incremental learning technique. This paper illustrates a self-adaptive SVM incremental learning algorithm derived from the structured description of data distribution. According to the geometric distance between the hyperplane and samples which contain original sample set and new sample set, a self-adaptive incremental sample selection model is established. This model can filter the boundary samples during the process of increment training accurately. The adjustment coefficients based on spatial distribution similarity are set up for new samples and original model samples in order to balance the speed and performance of incremental learning. Experimental results demonstrate that the proposed algorithm has higher training speed and better performance of classifications.

Key words: support vector machine (SVM), incremental learning, data distribution, hyperplane-distance