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Explanation-Based Neural Network Learning

A Lifelong Learning Approach

  • Book
  • © 1996

Overview

Authors:
  1. Sebastian Thrun

    1. Carnegie Mellon University, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 357)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Sebastian Thrun
    Pages 1-17

  3. Explanation-Based Neural Network Learning

    • Sebastian Thrun
    Pages 19-48

  4. The Invariance Approach

    • Sebastian Thrun
    Pages 49-92

  5. Reinforcement Learning

    • Sebastian Thrun
    Pages 93-129

  6. Empirical Results

    • Sebastian Thrun
    Pages 131-176

  7. Discussion

    • Sebastian Thrun
    Pages 177-193

  8. Back Matter

    Pages 195-264
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Keywords

About this book

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.

Authors and Affiliations

  • Carnegie Mellon University, USA

    Sebastian Thrun

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