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Readings in Machine Learning (The Morgan Kaufmann Series in Machine Learning) (Paperback)

by June Shaulik (Author), Thomas Dietterich (Author) "The three papers in this first chapter focus on the philosophical and methodological foundations of the machine learning field ..." (more)

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Product details

Paperback: 853 pages
Publisher: Morgan Kaufmann Publishers In (25 Dec 1990)
Language English
ISBN-10: 1558601430
ISBN-13: 978-1558601437
Product Dimensions: 27.8 x 21.6 x 4.4 cm
Average Customer Review: No customer reviews yet. Be the first.
Amazon.co.uk Sales Rank: 2,049,056 in Books (See Bestsellers in Books)

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Product Description

Product Description
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.

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Edited by Jude Shavlik and Thomas Dietterich

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The three papers in this first chapter focus on the philosophical and methodological foundations of the machine learning field. Read the first page

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Encyclopaedia of Library and...

This is the 68th volume (supplement 31) in a series which examines... Read more
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