《海量数据挖掘-王永利》Large Margin Distribution Machine(kdd 2014, ZhangZhou).pdfVIP

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《海量数据挖掘-王永利》Large Margin Distribution Machine(kdd 2014, ZhangZhou).pdf

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Large Margin Distribution Machine Teng Zhang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {zhangt, zhouzh}@ ABSTRACT Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classi?cation boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution. We characterize the margin distribution by the ?rst- and second-order statistics, i.e., the margin mean and variance. The LDM is a general learning approach which can be used in any place where SVM can be applied, and its superiority is veri?ed both theoretically and empirically in this paper. Categories and Subject Descriptors I.2.6 [Arti?cial Intelligence]: Learning; I.5.2 [Pattern Recognition]: Design Methodology—classi?er design and evaluation; H.2.8 [Database Management]: Database Applications—Data mining Keywords Margin distribution; minimum margin; classi?cation 1. INTRODUCTION Support Vector Machine (SVM) [5, 26] has always been one of the most successful learning algorithms. The basic idea is to identify a classi?cation boundary having a large margin for all the training examples, and the resultant optimization can be accomplished by a quadratic programming (QP) problem. Although SVMs have a long history of literatures, there are still great e?orts [16, 6, 25, 14, 8] on improving SVMs. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial adv