p范数正则化支持向量机分类算法

L2范数罚支持向量机(Support vector machine,SVM)是目前使用最广泛的分类器算法之一,同时实现特征选择和分类器构造的L1范数和L0范数罚SVM算法也已经提出.但是,这两个方法中,正则化阶次都是事先给定,预设p=2或p=1.而我们的实验研究显示,对于不同的数据,使用不同的正则化阶次,可以改进分类算法的预测准确率.本文提出p范数正则化SVM分类器算法设计新模式,正则化范数的阶次p可取范围为02范数罚SVM,L1范数罚SVM和L0范数罚SVM.

Classification Algorithm of Support Vector Machine via p-norm Regularization

The L₂ penalty support vector machine (SVM) algorithm is one of the most widely used learning algorithms, meanwhile L₁ norm and L₀ norm penalty support vector machines have been devised, which achieve simultaneously feature selection and classifier construction. However, in both methods, the regularization parameter is predetermined, i.e., the default p = 2 or p = 1. Our experimental study shows that different data, using a different regularization of order, can improve prediction accuracy of the classification algorithm. In this paper, new classifier design pattern of SVM based on p-norm regularization is proposed, where 02-norm, L₁-norm, and L₀-norm SVM.