Explanation-Based Neural Network Learning - A Lifelong Learning Approach (Paperback, Softcover reprint of the original 1st ed. 1996)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	The Springer International Series in Engineering and Computer Science, 357
Release date:	October 2011
First published:	1996
Authors:	Sebastian Thrun
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	264
Edition:	Softcover reprint of the original 1st ed. 1996
ISBN-13:	978-1-4612-8597-7
Categories:	Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	1-4612-8597-6
Barcode:	9781461285977

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