Explanation-Based Neural Network Learning: A Lifelong Learning Approach

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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 ...
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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.
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Product Details

Table of Contents

Foreword
Preface
1 Introduction 1
2 Explanation-Based Neural Network Learning 19
3 The Invariance Approach 49
4 Reinforcement Learning 93
5 Empirical Results 131
6 Discussion 177
A An Algorithm for Approximating Values and Slopes with Artificial Neural Networks 195
B Proofs of the Theorems 203
C Example Chess Games 207
References 227
List of Symbols 253
Index 259
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