This book constitutes the refereed proceedings of the 4th European Conference on Computational Learning Theory, EuroCOLT'99, held in Nordkirchen, Germany in March 1999. The 21 revised full papers presented were selected from a total of 35 submissions; also included are two invited contributions. The book is divided in topical sections on learning from queries and counterexamples, reinforcement learning, online learning and export advice, teaching and learning, inductive inference, and statistical theory of learning and pattern recognition.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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language defining framework, bounded finite thickness, leveraging algorithm, partitioning classification rule, ordinal mind change bound, arbitrary concept classes, relative mistake bounds, primitive recursive operators, range restricted expressions, proper equivalence queries, mind change bounds, monotone disjunctions, master hypotheses, master hypothesis, learning disjunctions, relative loss bounds, uniform convergence bounds, approximate fingerprints, arcing algorithms, indirect algorithms, elementary formal systems, margin vector, best disjunction, quantization functional, expected information gain Key Phrases - Capitalized Phrases (CAPs): (learn more)
Artificial Intelligence, New York, Aggregating Algorithm, Morgan Kaufmann, International Conference, Monte Carlo, Normalized Winnow, Weighted Average Algorithm, Yoav Freund, Lecture Notes, Krishna Rao, Neural Information Processing Systems, Peter Bartlett, University of California, Theory of Computing, John Shawe-Taylor, Neural Computation, Australian National University, Leo Breiman, Theoretical Computer Science, Academic Press, Cambridge University Press, Estimation of Dependences Based, European Conference, Let Pos New!
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language defining framework, bounded finite thickness, leveraging algorithm, partitioning classification rule, ordinal mind change bound, arbitrary concept classes, relative mistake bounds, primitive recursive operators, range restricted expressions, proper equivalence queries, mind change bounds, monotone disjunctions, master hypotheses, master hypothesis, learning disjunctions, relative loss bounds, uniform convergence bounds, approximate fingerprints, arcing algorithms, indirect algorithms, elementary formal systems, margin vector, best disjunction, quantization functional, expected information gain Key Phrases - Capitalized Phrases (CAPs): (learn more)
Artificial Intelligence, New York, Aggregating Algorithm, Morgan Kaufmann, International Conference, Monte Carlo, Normalized Winnow, Weighted Average Algorithm, Yoav Freund, Lecture Notes, Krishna Rao, Neural Information Processing Systems, Peter Bartlett, University of California, Theory of Computing, John Shawe-Taylor, Neural Computation, Australian National University, Leo Breiman, Theoretical Computer Science, Academic Press, Cambridge University Press, Estimation of Dependences Based, European Conference, Let Pos New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 47
books:
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1
book
cites this book:
- Inductive Logic Programming (Apic Studies in Data Processing) by Stephen Muggleton on 5 pages
- Statistical Learning Theory by Vladimir N. Vapnik on 4 pages
- The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik on 4 pages
- Computational Learning Theory (Cambridge Tracts in Theoretical Computer Science) by M. H. G. Anthony on page 272, page 284, and Back Matter
- Reinforcement Learning (The Springer International Series in Engineering and Computer Science) by Richard S. Sutton on page 11, page 17, and page 137
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- Pattern Classification (2nd Edition) by Richard O. Duda on page 513
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