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Explanation-Based Neural Network Learning: A Lifelong Learning Approach / Edition 1
     

Explanation-Based Neural Network Learning: A Lifelong Learning Approach / Edition 1

by Sebastian Thrun
 

ISBN-10: 0792397169

ISBN-13: 9780792397168

Pub. Date: 04/30/1996

Publisher: Springer US

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

Overview

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.

Product Details

ISBN-13:
9780792397168
Publisher:
Springer US
Publication date:
04/30/1996
Series:
The Springer International Series in Engineering and Computer Science , #357
Edition description:
1996
Pages:
264
Product dimensions:
9.21(w) x 6.14(h) x 0.69(d)

Table of Contents

Preface. 1. Introduction. 2. Explanation-Based Neural Network Learning. 3. The Invariance Approach. 4. Reinforcement Learning. 5. Empirical Results. 6. Discussion. A. An Algorithm for Approximating Values and Slopes with Artificial Neural Networks. B. Proofs of the Theorems. C. Example Chess Games. References. List of Symbols. Index.

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