An introduction to computational learning theory by Michael J. Kearns

Cover of: An introduction to computational learning theory | Michael J. Kearns

Published by MIT Press in Cambridge, Mass .

Written in English

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Subjects:

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

Edition Notes

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

Book details

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

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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 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.

Book Abstract: 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.

The book's writing style is clear and pleasant, reflecting the current trend toward intuitive, philosophical presentations of complex technical matters. Although readers should not expect to find plug-and-play algorithms, the book is recommended to everyone as a solid introduction to the theoretical aspects of computational learning.

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March The book contains full versions of the 28 papers accepted for presentation at the conference as.

An Introduction to Computational Learning Theory (The MIT Press) by Kearns, Michael J bad to start; this, despite the book's age (15 years in a 25 year old subfield), is most probably* a testament to the book's value as an approachable introduction. * (As usual, some positive probability is reserved to indict the field's lack of advancement /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.

Each topic in the book. Bayesian classifiers boosting computational learning theory decision trees genetic algorithms linear and polynomial classifiers nearest neighbor classifier neural networks performance evaluation reinforcement learning statistical learning time-varying classes, imbalanced representation artificial intelligence machine learning data mining deep.

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Each topic in the book has been chosen to elucidate a general principle, which 5/5(1). and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning.

Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational Size: 1MB. Introduction. The two instances of modern in the title of this book reflect the two major recent revolutions in biological data analyses.

Biology, formerly a science with sparse, often only qualitative data has turned into a field whose production of quantitative data is on par with high energy physics or astronomy, and whose data are wildly more heterogeneous and complex.

An Introduction to Computational Learning Theory 作者: Michael J. Kearns / Umesh V. Vazirani 出版社: The MIT Press 出版年: 页数: 定价: USD 装帧: Hardcover ISBN: /10(13). This is the easiest introduction to the theory of machine learning I've found, but it still requires a fair degree of knowledge of computer science, at the very least a grasp of computational complexity on the level of a good undergraduate course on the analysis of algorithms.

If that's in place, however, it makes a fine book for self-study. “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.

(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.

An Introduction to Computational Learning Theory: HSG Introduction to Computational Learning Theory, by M. Kearns and U. Vazirani. This book may be purchased at the Columbia Bookstore or online. Its an excellent book, but several topics we'll cover are not in the book.

Pointers to papers which will cover these topics will be given here. Introduction The first part of this book is an introduction to group begins with a study of permutation groups in chapter ically this was one of the starting points of group fact it was in the context of permutations of the roots of a polynomial that they first appeared (see).

Asecond starting point was. The book I refer is Introduction to Theory of Computation by John C. Martin [Introduction to Language and the Theory of Computation: John C. Martin]. If you are new to this subject and want to understand each concept with basics then I must recomm.

Introduction to Computational Intelligence. Nazmul Siddique. (CI) and then presents a discussion on the paradigms of computational intelligence. In this book, the three methodologies of fuzzy logic, neural networks and evolutionary computing are covered and all other methodologies (such as swarm intelligence, learning theory and.

Perceptrons: An Introduction to Computational Geometry is a book of thirteen chapters grouped into three sections. Chapters 1–10 present the authors' perceptron theory through proofs, Chapter 11 involves learning, Chapter 12 treats linear separation problems, and Chapter 13 discusses some of the authors' thoughts on simple and multilayer Author: Marvin Minsky, Seymour Papert.

An introduction to formal language and automata. Narosa Publishing. ISBN Michael Sipser (). Introduction to the Theory of Computation (3rd ed.). Cengage Learning. ISBN Eitan Gurari (). An Introduction to the Theory of Computation. Computer Science Press. ISBN Archived from the original on.

Introduction to Computational Economics (CPS ), Spring Details Book We will use parts of a new book by Shoham and Leyton-Brown (SLB), an early draft of which can be found in a subdirectory of this directory called book/ under the name SLB 10 introduction,introduction,9/18, 9/ Introduction to Computational Mathematics The goal of computational mathematics, put simply, is to find or develop algo-rithms that solve mathematical problems computationally (ie.

using comput-ers). In particular, we desire that any algorithm we develop fulfills four primary properties: •. The book can serve as a text for a graduate complexity course that prepares graduate students interested in theory to do research in complexity and related areas.

Such a course can use parts of Part I to review basic material, and then move on to the advanced topics of Parts II and III. The book contains far more material than can be taught.

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With that said, remember that this book is just a semester-long introduction to a vast landscape. I recommend the following books for more depth: Peter Linz, "Introduction to Formal Languages and Automata"; Nigel Cutland, "Introduction to Computability Theory"; Christos Papadimitriou, "Computational Complexity".5/5(5).Learning is regarded as the phenomenon of knowledge acquisition in the absence of explicit programming.

A precise methodology is given for studying this phenomenon rom a .

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