Last edited by Visho
Thursday, May 14, 2020 | History

6 edition of An introduction to computational learning theory found in the catalog.

An introduction to computational learning theory

by Michael J. Kearns

  • 82 Want to read
  • 17 Currently reading

Published by MIT Press in Cambridge, Mass .
Written in English

    Subjects:
  • Machine learning.,
  • Artificial intelligence.,
  • Algorithms.,
  • Neural networks (Computer science)

  • Edition Notes

    Includes bibliographical references (p. [193]-203) and index.

    StatementMichael J. Kearns, Umesh V. Vazirani.
    ContributionsVazirani, Umesh Virkumar.
    Classifications
    LC ClassificationsQ325.5 .K44 1994
    The Physical Object
    Paginationxii, 207 p. :
    Number of Pages207
    ID Numbers
    Open LibraryOL1092263M
    ISBN 100262111934
    LC Control Number94016588

    “Introduction to Computational Science is a marvelous introduction to the field, suitable even for beginning undergraduates and full of wonderful examples.” “Application modules draw from biology, physics, chemistry and economics, with biology and physics dominating somewhat.   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 : MIT Press.

    1. (draft) Introduction to Online Convex Optimization, by E. Hazan, available here 2. An Introduction To Computational Learning Theory, by M.J. Kearns and U. Vazirani 3. Prediction, Learning and Games, by N. Cesa-Bianchi and G. Lugosi 4. Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David 5. Avi Wigderson Mathematics and Computation Draft: Ma Acknowledgments In this book I tried to present some of the knowledge and understanding I acquired in my four decades in the eld. The main source of this knowledge was the Theory of Computation commu-nity, which has been my academic and social home throughout this period.

    This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. J Theory of Computation (Fall ) Related Content. Course Sequences. This course is the second part of a two-course sequence. The first course in the sequence is J Automata, Computability, and Complexity. Course Collections. See related courses in the following collections: Find Courses by Topic.


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An introduction to computational learning theory by Michael J. Kearns Download PDF EPUB FB2

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Each topic in the book has been chosen to elucidate a general principle, which Cited by: 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. 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/5.

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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 ational learning theory is a new and rapidly expanding area of research that examines formal models of.

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This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLTheld in Amsterdam, The Netherlands, in July The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions.

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.

An Introduction to Computational Learning Theory. 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.

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