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9

ICML
2005
IEEE

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A comparison of tight generalization error bounds

14 years 5 months ago

A comparison of tight generalization error bounds

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We investigate the empirical applicability of several bounds (a number of which are new) on the true error rate of learned classifiers which hold whenever the examples are chosen independently at random from a fixed distribution. The collection of tricks we use includes:

John Langford, Matti Kääriäinen

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ICML 2005 | Machine Learning | True Error Rate |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2005
Where	ICML
Authors	John Langford, Matti Kääriäinen

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