Explanation-Based Neural Network Learning : A Lifelong Learning Approach
Language: English
Published by Springer, 2011
Series: Book 1 of 1 - The Springer International Series in Engineering and Computer Science
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9781461285977
- Title
- Explanation-Based Neural Network Learning : A Lifelong Learning Approach
- Author
- Sebastian Thrun
- Publisher
- Springer
- 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.
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