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32

ICML
2001
IEEE

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An Improved Predictive Accuracy Bound for Averaging Classifiers

14 years 11 months ago

An Improved Predictive Accuracy Bound for Averaging Classifiers

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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.

John Langford, Matthias Seeger, Nimrod Megiddo

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ICML 2001 | Improved Bound | Machine Learning | Maximum Entropy Discrimination | Popular Averaging Techniques |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2001
Where	ICML
Authors	John Langford, Matthias Seeger, Nimrod Megiddo

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