Margin distribution and learning algorithms
Margin distribution and learning algorithms
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保证金分布和学习算法
DOI：
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发表时间：
2003
期刊：
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通讯作者：
D. Roth
中科院分区：
文献类型：
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作者：
A. Garg;D. Roth
最近的理论结果表明，通过显式地考虑训练数据的观测余量分布，可以得到改进的分类器泛化误差边界。目前，在实践中使用的算法没有利用边际分布，而是通过相对于最接近超平面的点的优化来驱动。
Recent theoretical results have shown that improved bounds on generalization error of classifiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution and are driven by optimization with respect to the points that are closest to the hyperplane.
This paper enhances earlier theoretical results and derives a practical data-dependent complexity measure for learning. The new complexity measure is a function of the observed margin distribution of the data, and can be used, as we show, as a model selection criterion. We then present the Margin Distribution Optimization (MDO) learning algorithm, that directly optimizes this complexity measure. Empirical evaluation of MDO demonstrates that it consistently outperforms SVM.