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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 109 (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...
Combining Train Set and Test Set Bounds
 In: Proc. 19th International Conference on Machine Learning
"... This paper is about bounds on future error rates. We present a theorem for combining an arbitrary test set based bound with an arbitrary training set based bound. Appropriate use of this theorem results in a combined bound with two properties: 1) the combined bound is never much worse than ei ..."
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Cited by 6 (2 self)
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This paper is about bounds on future error rates. We present a theorem for combining an arbitrary test set based bound with an arbitrary training set based bound. Appropriate use of this theorem results in a combined bound with two properties: 1) the combined bound is never much worse than either the training set based bound or the test set based bound and 2) the combined bound is sometimes better than either bound individually. Empirical validation is presented showing the effectiveness of the combined bound.
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
Sample Complexity of Classification
"... We discuss basic sample complexity theory and it's impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. There are a two important implications of the results presented here: (1) Common practices for reporting results ..."
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We discuss basic sample complexity theory and it's impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. There are a two important implications of the results presented here: (1) Common practices for reporting results in classification should change to us the test set bound. (2) Train set bounds can sometimes be used to directly motivate learning algorithms.