Readings in Machine Learning (The Morgan Kaufmann Series in Machine Learning)
Language: English
Published by Morgan Kaufmann Publishers In, 1990
- Softcover
- Used
Condition: Used - Fine
US$ 365.04
Quantity: 1 available
Add to basketItem description from seller
Zustand: Sehr gut | Seiten: 853 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Seller Inventory # 41400518/202
- Title
- Readings in Machine Learning (The Morgan Kaufmann Series in Machine Learning)
- Author
- Shavlik, Jude W.
- Publisher
- Morgan Kaufmann Publishers In
- Publication year
- 1990
- Condition
- Sehr gut
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1558601430
- ISBN 13
- 9781558601437
- Seller catalogs
- Bücher
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.
"Synopsis" may belong to another edition of this title.
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 title" may belong to another edition of this title.
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