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

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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.
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Editorial Reviews

Booknews
Describes a paradigm for machine learning that may open a new generation of methods, especially for situations in which a series of different learning tasks provides an opportunity for synergy among them. The explanation-based neural network approach transfers knowledge across multiple learning tasks, allowing domain knowledge accumulated in previous learning efforts to guide generalization in new learning tasks. The result is more accurate generalizations with less data than previous methods. The method is demonstrated in contexts of supervised learning, reinforced learning, robotics, and chess. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

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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