一种加权线性损失支持向量机

A WEIGHTED LINEAR LOSS SUPPORT VECTOR MACHINE

针对传统支持向量机无法适应大规模问题,通过引入加权线性损失函数,取代标准支持向量机的Hinge损失,提出一种加权线性损失支持向量机WLSVM(Weighted Linear Loss Support Vector Machine)。它的主要方法是:(1)通过对线性损失增加权重,提出对不同位置上的训练点给出不同惩罚,在一定程度上避免了过度拟合,增强了泛化能力。(2)仅需计算非常简单的数学表达式就可获得分类超平面,且方便解决大规模问题。通过在合成和真实数据集上的试验,结果表明:WLSVM的分类精度高于SVM和LSSVM,且减少了计算时间,尤其对于大规模问题。

In view of that traditional support vector machine（ SVM） cannot adapt to large-scale problems,by introducing the weighted linear loss function to replace the Hinge loss function in standard support vector machine,the paper proposes a weighted linear loss support vector machine（ WLSVM）. Its main features are：（ 1） by adding the weight on linear loss,it suggests giving different penalties on training points at different positions,thus avoids over fitting to a certain extent and enhances the generalisation ability.（ 2） it can obtain the classified hyperplane by just calculating every simple mathematical expression,and is convenient in solving large-scale problems. Through the experiments on both synthetic and real datasets it is shown that the classification accuracy of WLSVM is higher than that of SVM and LS-SVM,and the computation time is reduced as well,especially for large-scale problems.