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Improved Generalization Through Explicit Optimization of Margins

Mason, Llew; Bartlett, Peter; Baxter, Jon

Description

Recent theoretical results have shown that the generalization performance of thresholded convex combinations of base classifiers is greatly improved if the underlying convex combination has large margins on the training data (i.e., correct examples are classified well away from the decision boundary). Neural network algorithms and AdaBoost have been shown to implicitly maximize margins, thus providing some theoretical justification for their remarkably good generalization performance. In this...[Show more]

CollectionsANU Research Publications
Date published: 2000
Type: Journal article
URI: http://hdl.handle.net/1885/89538
Source: Machine Learning
DOI: 10.1023/A:1007697429651

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