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4 of 4 people found the following review helpful:
5.0 out of 5 stars
Absolute must for any work in the field.,
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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.
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Readings in Machine Learning (Morgan Kaufmann Series in Machine Learning) by Jude Shavlik (Paperback - June 15, 1990)
Used & New from: $14.99
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