Skip navigation
Skip navigation

Improved Generalization Through Explicit Optimization of Margins

Mason, Llew; Bartlett, Peter; Baxter, Jon


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
Source: Machine Learning
DOI: 10.1023/A:1007697429651


File Description SizeFormat Image
01_Mason_Improved_Generalization_2000.pdf116.16 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator