支持向量机的快速分类算法

支持向量机（SVM）算法在训练集的规模很大特别是支持向量很多时，支持向量机的学习过程需要占用大量的内存，算法的速度较慢。为此，笔者提出一种新的SVM快速分类算法。该算法通过选择边界向量，构造新的训练样本，减少了参与训练的样本数目。实验证明，该算法不仅能保证原算法的精度，具有良好的推广能力，而且提高了算法的速度。

Fast Classification Algorithm for Support Vector Machine

For support vector machine （SVM）, when training set is very large, especially when there are many support vectors, the process of learning requires a great deal of EMS memory, and the speed of count is very slow. In this paper, a new fast classification algorithm is presented to train SVM by selecting border vectors which may be the support vectors, so as to reduce train- ing samples and to increase training speed. Experiment results show that the algorithm not only acquires the same precision with that of the classical algorithms, but also has better performance and is faster than that of the classical algorithms, especially in the case of having large number of training samples.