特征空间最小数据分布最大间隔支持向量机

摘要

为提高支持向量机的泛化能力,根据统计学习理论中学习机推广能力的界和VC维理论,提出了一种同时以特征空间中样本数据分布半径最小化和类间间隔最大化为优化目标的支持向量机模型.通过合理设计其目标函数,将该支持向量机的建模问题转化为二次规划问题,从而可以采用与传统SVM相似的算法快速实现.用UCI数据库中的部分数据进行了仿真,实验证明与传统的SVM相比,在分类准确度不降低,且有所提高的基础上,使其支持向量的数目得到减少;在支持向量数目相近的情况下,预测精度得到提高.体现出更强的泛化能力.%In order to improve the generalization capability of support vector machine, a new support vector machine with minimum-data distribution and maximum-margin in feature space was presented based on the bounds for the generalization error in machine-learning and the VC dimension theory. The support vector machine ( SVM) optimization problem here was treated as a quadratic programming problem after reformulating a reasonable objective function, so the problem was solved by applying algorithms similar to that of the traditional SVM. Simulation using UCI datasets shows that compared to the traditional SVM, the improved SVM classifier reduces the support vectors number with the same or even higher prediction accuracy; on the other hand, it improves the prediction accuracy with similar support vectors number. The simulation results show that the proposed SVM have more generalization ability.