This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.
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Combinatorial auctions (CAs) have recently emerged as a possible mechanism to improve economic efficiency when many items are on sale. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
polynomial threshold function, indexable class, label efficient prediction, empirical minimizer, efficient forecaster, limit with probability one, graphical economy, universal sequence prediction, uniformly recursive family, learning parity functions, maximum margin solution, graphical equilibrium, empirical play, grand bundle, sparse multivariate polynomials, unnormalized case, elicitation algorithm, learning intersections, representer theorem, cleanup round, oracle inequalities, mean voter theorem, graphical games, superset queries, regret bounds Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, New York, Neural Computation, Annual Conference, Tukey Depth, Lim Txtrec, Tukey Median, Neural Information Processing Systems, Annual Symposium, Bayes Depth, Cambridge University Press, Foundations of Computer Science, John Wiley, Morgan Kaufmann, Springer Verlag, Las Vegas, Monte Carlo, Perceptron Convergence Theorem, Stone's Theorem, The Annals of Statistics, University of California, Consv Txtrec, Department of Statistics, Chih-Jen Lin, Hans Ulrich Simon New!
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Combinatorial auctions (CAs) have recently emerged as a possible mechanism to improve economic efficiency when many items are on sale. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
polynomial threshold function, indexable class, label efficient prediction, empirical minimizer, efficient forecaster, limit with probability one, graphical economy, universal sequence prediction, uniformly recursive family, learning parity functions, maximum margin solution, graphical equilibrium, empirical play, grand bundle, sparse multivariate polynomials, unnormalized case, elicitation algorithm, learning intersections, representer theorem, cleanup round, oracle inequalities, mean voter theorem, graphical games, superset queries, regret bounds Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, New York, Neural Computation, Annual Conference, Tukey Depth, Lim Txtrec, Tukey Median, Neural Information Processing Systems, Annual Symposium, Bayes Depth, Cambridge University Press, Foundations of Computer Science, John Wiley, Morgan Kaufmann, Springer Verlag, Las Vegas, Monte Carlo, Perceptron Convergence Theorem, Stone's Theorem, The Annals of Statistics, University of California, Consv Txtrec, Department of Statistics, Chih-Jen Lin, Hans Ulrich Simon New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
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This book cites 74
books:
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- Statistical Learning Theory by Vladimir N. Vapnik on 7 pages
- Neural Network Learning: Theoretical Foundations by Martin Anthony on page 406, page 426, and page 548
- Computational Learning Theory by Ma Fulk in Front Matter, and page 406
- Algorithmic Learning Theory by Nicoḷ Cesa-Bianchi on page 471, and Back Matter
- Machine Learning by Tom M. Mitchell on page 238, and page 638
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