An Introduction to Computational Learning Theory

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An Introduction to Computational Learning Theory

An Introduction to Computational Learning Theory

  • Author: Michael J. Kearns,Umesh Virkumar Vazirani,Umesh Vazirani
  • Publisher: MIT Press
  • ISBN: 9780262111935
  • Category: Computers
  • Page: 207
  • View: 8985
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Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

  • Author: M. H. G. Anthony,N. Biggs
  • Publisher: Cambridge University Press
  • ISBN: 9780521599221
  • Category: Computers
  • Page: 157
  • View: 5233
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This an introduction to the theory of computational learning.

Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings

  • Author: Paul Vitanyi
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540591191
  • Category: Computers
  • Page: 414
  • View: 5958
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This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995. The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.

Proceedings of the Third Annual Workshop on Computational Learning Theory

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Proceedings of the Third Annual Workshop on Computational Learning Theory

Proceedings of the Third Annual Workshop on Computational Learning Theory

University of Rochester, Rochester, New York, August 6-8, 1990

  • Author: ACM Special Interest Group for Automata and Computability Theory,SIGART.
  • Publisher: Morgan Kaufmann
  • ISBN: N.A
  • Category: Computers
  • Page: 395
  • View: 8351
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Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings

  • Author: Shai Ben-David
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540626855
  • Category: Computers
  • Page: 330
  • View: 5003
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Content Description #Includes bibliographical references and index.

Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, Proceedings

  • Author: David Helmbold,Bob Williamson
  • Publisher: Springer
  • ISBN: 3540445811
  • Category: Computers
  • Page: 638
  • View: 4441
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This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLT 2001, held in Amsterdam, The Netherlands, in July 2001.The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions. All current aspects of computational learning and its applications in a variety of fields are addressed.

Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings

  • Author: Jyrki Kivinen
  • Publisher: Springer Science & Business Media
  • ISBN: 354043836X
  • Category: Computers
  • Page: 395
  • View: 4961
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This book is tailored for students and professionals as well as novices from other fields to mass spectrometry. It will guide them from the basics to the successful application of mass spectrometry in their daily research. Starting from the very principles of gas-phase ion chemistry and isotopic properties, it leads through the design of mass analyzers and ionization methods in use to mass spectral interpretation and coupling techniques. Step by step the readers will learn how mass spectrometry works and what it can do as a powerful tool in their hands. The book comprises a balanced mixture of practice-oriented information and theoretical background. The clear layout, a wealth of high-quality figures and a database of exercises and solutions, accessible via the publisher's web site, support teaching and learning.

Computational Learning Theory

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Computational Learning Theory

Computational Learning Theory

4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings

  • Author: Paul Fischer
  • Publisher: Springer Science & Business Media
  • ISBN: 3540657010
  • Category: Computers
  • Page: 299
  • View: 811
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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.

Computational Learning Theory and Natural Learning Systems: Making learning systems practical

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Computational Learning Theory and Natural Learning Systems: Making learning systems practical

Computational Learning Theory and Natural Learning Systems: Making learning systems practical

  • Author: Russell Greiner,Stephen José Hanson,Thomas Petsche
  • Publisher: MIT Press
  • ISBN: 9780262571180
  • Category: Computers
  • Page: 407
  • View: 4310
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This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors: Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador

Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment

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Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment

Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment

  • Author: Stephen José Hanson,Ronald L. Rivest
  • Publisher: Mit Press
  • ISBN: 9780262581332
  • Category: Computers
  • Page: 449
  • View: 1993
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Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.