通过特征权学习增强支持向量机的泛化能力

在分类问题中,支持向量机(SVM)首先将样本映入某一高维特征空间,并在此空间中构造具有最大间隔的分类超平面.由Vapnik的统计学习理论知道,SVM泛化能力的强弱与分类超平面间隔的大小有十分密切的关系:分类平面的间隔越大,SVM的泛化能力就越强.本文提出了一种通过特征权学习来增加分类超平面的间隔,从而增强SVM泛化能力的方法.仿真试验表明,该方法对提高SVM的泛化能力是有效的.

Enhancing Generalization Capability of SVM Classifiers with Feature Weight Adjustment

A SVM constructs an optimal separating hyper-plane through maximizing the margin between two classes in high-dimensional feature space.Based on statistical learning theory,the margin scale reflects the generalization capability to a great extent.The bigger the margin scale takes,the better the generalization capability of SVMs will have.This paper makes an attempt to enlarge the margin between two support vector hyper-planes by feature weight adjustment.The experiments demonstrate that our proposed techniques in this paper can enhance the generalization capability of the original SVM classifiers.