A Fast Support Vector Classification Algorithm Based on the Sort of Nearest Neighborhood Information Measure
基于类间最近邻支持向量信息测度排序的快速分类算法研究

To improve the training speed performance of large-scale support vector machine(SVM), a fast algorithm is proposed in this paper by exploiting the geometric distribution of support vector in feature space. A support vector information measure definition based on the nearest inter-classes distance is set up and a sort process is presented. Then a reduced number of sample subspace is extracted for support vector training. In addition, instead of the traditional quadratic programming, multiplicative update is used to solve Lagrange multiplier in optimizing the solution of support vector. The samples of rest are used for cross validating till the algorithm is convergence. Experimental results demonstrate that this method has better performance and has overcome the flaw of standard SVM. This algorithm could greatly reduce the computational load and increase the speed of training, especially in the case of large number of training samples.