An Improved Predictive Accuracy Bound for Averaging Classifiers

We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popular averaging techniques such as Bayesian classification, Maximum Entropy discrimination, Winnow and Bayes point machines and has implications for learning algorithm design.


Published in:
Proceedings of the 18th International Conference on Machine Learning, 290-297
Presented at:
International Conference on Machine Learning 18
Year:
2001
Keywords:
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 Record created 2010-12-01, last modified 2018-09-25

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