The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989)

Kearns, Michael J.

ISBN 10: 0262111527 / ISBN 13: 9780262111522

Published by Mit Pr, 1990

About this Item

Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

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Bibliographic Details

• Title: The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989)
• Publisher: Mit Pr
• Publication Date: 1990
• Language: English
• ISBN 10: 0262111527
• ISBN 13: 9780262111522
• Binding: Hardcover
• Condition: Good

About this title

Synopsis

The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning.

Theorems are presented that help elucidate the boundary of what is efficiently learnable from examples. These results take the form of both algorithms with proofs of their performance, and hardness results demonstrating the intractability of learning in certain natural settings. In addition the book contains lower bounds on the resources required for learning, an extensive study of learning in the presence of errors in the sample data, and several theorems demonstrating reducibilities between learning problems.

Michael J. Kearns is Postdoctoral Associate in the Laboratory for Computer Science at MIT.

Contents: Definitions, Notations, and Motivation. Overview of Recent Research in Computational Learning Theory. Useful Tools for Distribution-Free Learning. Learning in the Presence of Errors. Lower Bounds on Sample Complexity. Cryptographic Limitations on Polynomial-Time Learning. Distribution-Specific Learning in Polynomial Time. Equivalence of Weak Learning and Group Learning.

About the Author

Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.