【摘要】 以L1范数为例,设计了一个L1范数的大间隔分类器L1MMC(L1-norm Maximum Margin Classifier),主要特点如下:(1)间隔由L1范数的点到平面距离解析表示;(2)该分类器与SVM一样,通过最大化L1间隔,达到同时最小化经验风险和结构风险的目的;(3)只需要通过线性规划进行求解,避免了SVM的二次规划问题;(4)分类精度达到甚至超过SVM.最后,在人工数据和国际标准UCI数据集上,验证了该方法的有效性.更多还原

【Abstract】 L1 norm is taken as an example to design an L1 norm L1 MMC(L1-norm Maximum Margin Classifier).The main features are as follows:(1) The interval is represented by the point-to-plane distance analysis of the L1 norm;(2) This classifier,like SVM,maximizes the L1 interval to minimize the risk of both empirical and structural risks;(3) Only need to be solved through linear programming to avoid the quadratic programming problem of SVM;(4) Classification accuracy reaches or even exceeds SVM.Finally,on the artificial data and the international standard UCI data set,verify the effectiveness of the method.更多还原