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Tutorial on Practical Prediction Theory for Classification

by: John Langford

Journal of Machine Learning Research, Vol. 6 (March 2005), pp. 273-306.

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JMLR (PDF), http://hunch.net/~jl/projects/prediction_…

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Abstract

We discuss basic prediction theory and its 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 quantitatively useful.

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@article{citeulike:2712765, abstract = {We discuss basic prediction theory and its 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 quantitatively useful.}, author = {Langford, John }, citeulike-article-id = {2712765}, journal = {Journal of Machine Learning Research}, keywords = {error-estimation, learning-bounds, ml-foundations}, month = {March}, pages = {273--306}, posted-at = {2008-04-24 12:37:12}, priority = {2}, title = {Tutorial on Practical Prediction Theory for Classification}, url = {http://www.jmlr.org/papers/volume6/langford05a/langford05a.pdf}, volume = {6}, year = {2005} }

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