2014 LargeMarginDistributionMachine

• (Zhang & Zhou, 2014) ⇒ Teng Zhang, and Zhi-Hua Zhou. (2014). “Large Margin Distribution Machine.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623710

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Large+Margin+Distribution+Machine
• http://dl.acm.org/citation.cfm?id=2623330.2623710&preflayout=flat#citedby

Quotes

Author Keywords

• Classification; induction; margin distribution; minimum margin

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 classification 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 first - 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 verified both theoretically and empirically in this paper.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LargeMarginDistributionMachine	Zhi-Hua Zhou Teng Zhang			Large Margin Distribution Machine				10.1145/2623330.2623710		2014