Explanation-Based Neural Network Learning
Language: English
Published by Springer, Springer Okt 2011, 2011
Series: Book 1 of 1 - The Springer International Series in Engineering and Computer Science
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
AbeBooks seller since January 23, 2017
Condition: New
£ 141.57
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This item is printed on demand - Print on Demand Titel. Neuware -Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess.`The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.'From the Foreword by Tom M. Mitchell.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 284 pp. Englisch.…
Seller Inventory # 9781461285977
- Title
- Explanation-Based Neural Network Learning
- Author
- Sebastian Thrun
- Publisher
- Springer, Springer Okt 2011
- Publication year
- 2011
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1461285976
- ISBN 13
- 9781461285977
- Item weight
- 435 grams
- Dimensions
- 235x155x16 mm
- Series
- Book 1 of 1: The Springer International Series in Engineering and Computer Science
`The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.'
From the Foreword by Tom M. Mitchell.
"Synopsis" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
Shipping rates from Germany to U.S.A.
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