Hardcover. 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. This book describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics and chess. Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N� de ref. del art�culo 9780792397168

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Detalles bibliogr�ficos

T�tulo: Explanation-Based Neural Network Learning (Hardcover)
Editor: Kluwer Academic Publishers, Dordrecht
Fecha de publicaci�n: 1996
Idioma: Ingl�s
ISBN 10: 0792397169
ISBN 13: 9780792397168
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

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.

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