This book constitutes the refereed proceedings of the 13th International Conference on Algorithmic Learning Theory, ALT 2002, held in Lübeck, Germany in November 2002. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.
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Learning theory of mathematical or logical concepts seem to be one of the main research targets of learning theory and its applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
extended pattern languages, individual strangeness measure, agnostic boosting, complementary matrix graph, inferable from positive examples, linear sorting functions, erasing regular pattern languages, residue hypotheses, predictive complexity, enumerable subspaces, generalized logical consequences, reflective inductive inference, polynomial time inferable, empirical performance curve, minimal language problem, inferable from positive data, polynomial query complexity, ordinal counter, guessing function, nonnull ordinals, ordered term tree, term tree languages, residue hypothesis, quasipolynomial time, query learner Key Phrases - Capitalized Phrases (CAPs): (learn more)
Computer Science, Artificial Intelligence, Springer-Verlag Berlin Heidelberg, Lecture Notes, Annual Conference, New York, International Conference, Morgan Kaufmann, Connection Method, Cambridge University Press, Multiplicative Relevant Logic, Frank Stephan, Annual Symposium, Annual Workshop, Neural Computation, International Workshop, Jurgen Forster, Neural Information Processing Systems, Sanjay Jain, Theory of Computing, Bottom Method, Classical Recursion Theory, Inductive Rule Learning, Mathematisches Institut, New Generation Computing New!
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Learning theory of mathematical or logical concepts seem to be one of the main research targets of learning theory and its applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
extended pattern languages, individual strangeness measure, agnostic boosting, complementary matrix graph, inferable from positive examples, linear sorting functions, erasing regular pattern languages, residue hypotheses, predictive complexity, enumerable subspaces, generalized logical consequences, reflective inductive inference, polynomial time inferable, empirical performance curve, minimal language problem, inferable from positive data, polynomial query complexity, ordinal counter, guessing function, nonnull ordinals, ordered term tree, term tree languages, residue hypothesis, quasipolynomial time, query learner Key Phrases - Capitalized Phrases (CAPs): (learn more)
Computer Science, Artificial Intelligence, Springer-Verlag Berlin Heidelberg, Lecture Notes, Annual Conference, New York, International Conference, Morgan Kaufmann, Connection Method, Cambridge University Press, Multiplicative Relevant Logic, Frank Stephan, Annual Symposium, Annual Workshop, Neural Computation, International Workshop, Jurgen Forster, Neural Information Processing Systems, Sanjay Jain, Theory of Computing, Bottom Method, Classical Recursion Theory, Inductive Rule Learning, Mathematisches Institut, New Generation Computing New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 69
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- Theory of Recursive Functions and Effective Computability by Hartley Rogers on 4 pages
- Statistical Learning Theory by Vladimir N. Vapnik on 4 pages
- Classical Recursion Theory: The Theory of Functions and Sets of Natural Numbers, Vol. 1 (Studies in Logic and the Foundations of Mathematics, Vol. 125) by Piergiorgio Odifreddi on 4 pages
- Machine Translation: From Research to Real Users: 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002 Tiburon, CA, ... / Lecture Notes in Artificial Intelligence) by Stephen D. Richardson in Back Matter (1), and Back Matter (2)
- Advances in Artificial Intelligence by Guilherme Bittencourt in Back Matter (1), and Back Matter (2)
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- Advanced Lectures on Machine Learning: Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures (Lecture Notes in Computer Science) by Shahar Mendelson in Back Matter (1), Back Matter (2), and Back Matter (3)
- Computational Learning Theory: 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings ... / Lecture Notes in Artificial Intelligence) by Jyrki Kivinen on page 104, and page 119
- Autonomous Dynamic Reconfiguration in Multi-Agent Systems: Improving the Quality and Efficiency of Collaborative Problem Solving (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Markus Hannebauer in Back Matter (1), and Back Matter (2)
- AI 2002: Advances in Artificial Intelligence by Bob McKay in Back Matter (1), and Back Matter (2)
- Mental Representation and Processing of Geographic Knowledge: A Computational Approach (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) by Thomas Barkowsky in Back Matter (1), and Back Matter (2)
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