Readings in Machine Learning (Morgan Kaufmann Series in Machine Learning) [Paperback]

Jude Shavlik (Editor), Thomas Dietterich (Editor)

Book Description

Publication Date: June 15, 1990 | ISBN-10: 1558601430 | ISBN-13: 978-1558601437

The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business.

Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.

Editorial Reviews

From the Back Cover

The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business.

Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.

About the Author

Edited by Jude Shavlik and Thomas Dietterich

Product Details

• Paperback: 853 pages
• Publisher: Morgan Kaufmann (June 15, 1990)
• Language: English
• ISBN-10: 1558601430
• ISBN-13: 978-1558601437
• Product Dimensions: 10.9 x 8.5 x 1.7 inches
• Shipping Weight: 4.3 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,872,890 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

4 of 4 people found the following review helpful:

5.0 out of 5 stars Absolute must for any work in the field., January 28, 2002

By 

Mihailo Despotovic (Silicon Valley, CA USA) - See all my reviews
(REAL NAME)   

This review is from: Readings in Machine Learning (Morgan Kaufmann Series in Machine Learning) (Paperback)

The aim of the book is to bring together key papers in Machine Learning and to provide an introduction to the field and a reference collection for graduate students and researchers. The book contains 51 most imoportant article from Machine Learning (up to 1990). Most of these are NOT available online, so watch out! The following areas are covered: Introduction (3 papers; one by Simon), Inductive Learning From Preclassified Training Examples (16 papers including great classics from Quinlan, Michalski, Mitchell, Minsky...), Unsupervided Learning and Concept Discovery (9 papers -- Feigenbaum, Holland...), Improving the Efiiciency of a Problem Solver (10 papers including fameous Samuel's gem "Some Studies in Machine Learning Using the Game of Checkers"; also papers from Mitchell, Nillson, Utgoff...), Using Preexisting Domain Knowledge Inductively (13 papers; Russel, etc...). Really really outstanding collection and a definite recommendation.