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Tutorial on Practical Prediction Theory for Classification
, 2005
"... We discuss basic prediction theory and it's impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. This tutorial is meant to be a comprehensive compilation of results which are both theoretically rigorous and practicall ..."
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Cited by 86 (3 self)
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We discuss basic prediction theory and it's impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. This tutorial is meant to be a comprehensive compilation of results which are both theoretically rigorous and practically useful. There are two important implications...
Quantitatively Tight Sample Complexity Bounds
, 2002
"... This document is primarily about the theory of sample complexity for answering the question "Have we learned?". However, we do not neglect the experimental side. In particular, following the theory we will present results for application of sample complexity bounds to machine learning prob ..."
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Cited by 5 (1 self)
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This document is primarily about the theory of sample complexity for answering the question "Have we learned?". However, we do not neglect the experimental side. In particular, following the theory we will present results for application of sample complexity bounds to machine learning problems. These results are the 'best known results' in terms of bound tightness and should be considered as a guide and challenge to others working on sample complexity bounds
A Note on the PAC Bayesian Theorem
, 2006
"... We prove general exponential moment inequalities for averages of [0,1]valued iid random variables and use them to tighten the PAC Bayesian Theorem. The logarithmic dependence on the sample count in the enumerator of the PAC Bayesian bound is halved. 1 ..."
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Cited by 2 (0 self)
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We prove general exponential moment inequalities for averages of [0,1]valued iid random variables and use them to tighten the PAC Bayesian Theorem. The logarithmic dependence on the sample count in the enumerator of the PAC Bayesian bound is halved. 1