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Learning to Learn

  • Book
  • © 1998

Overview

Editors:
  1. Sebastian Thrun

    1. Carnegie Mellon University, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Lorien Pratt

    1. Evolving Systems, Inc., USA

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    You can also search for this editor in PubMed   Google Scholar

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-viii
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  2. Overview Articles

    1. Front Matter

      Pages 1-1
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    2. Learning to Learn: Introduction and Overview

      • Sebastian Thrun, Lorien Pratt
      Pages 3-17

    3. A Survey of Connectionist Network Reuse Through Transfer

      • Lorien Pratt, Barbara Jennings
      Pages 19-43

    4. Transfer in Cognition

      • Anthony Robins
      Pages 45-67

  3. Prediction/Supervised Learning

    1. Front Matter

      Pages 69-69
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    2. Theoretical Models of Learning to Learn

      • Jonathan Baxter
      Pages 71-94

    3. Multitask Learning

      • Rich Caruana
      Pages 95-133

    4. Making a Low-Dimensional Representation Suitable for Diverse Tasks

      • Nathan Intrator, Shimon Edelman
      Pages 135-157

    5. The Canonical Distortion Measure for Vector Quantization and Function Approximation

      • Jonathan Baxter
      Pages 159-179

    6. Lifelong Learning Algorithms

      • Sebastian Thrun
      Pages 181-209

  4. Relatedness

    1. Front Matter

      Pages 211-211
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    2. The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness

      • Daniel L. Silver, Robert E. Mercer
      Pages 213-233

    3. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge

      • Sebastian Thrun, Joseph O’Sullivan
      Pages 235-257

  5. Control

    1. Front Matter

      Pages 259-259
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    2. Child: A First Step Towards Continual Learning

      • Mark B. Ring
      Pages 261-292

    3. Reinforcement Learning with Self-Modifying Policies

      • Jürgen Schmidhuber, Jieyu Zhao, Nicol N. Schraudolph
      Pages 293-309

    4. Creating Advice-Taking Reinforcement Learners

      • Richard Maclin, Jude W. Shavlik
      Pages 311-347

  6. Back Matter

    Pages 349-354
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Keywords

About this book

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications.
Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it.
To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing.
A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications.
Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

Editors and Affiliations

  • Carnegie Mellon University, USA

    Sebastian Thrun

  • Evolving Systems, Inc., USA

    Lorien Pratt

Bibliographic Information

  • Book Title: Learning to Learn

  • Editors: Sebastian Thrun, Lorien Pratt

  • DOI: https://doi.org/10.1007/978-1-4615-5529-2

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1998

  • Hardcover ISBN: 978-0-7923-8047-4Published: 31 October 1997

  • Softcover ISBN: 978-1-4613-7527-2Published: 04 October 2012

  • eBook ISBN: 978-1-4615-5529-2Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: VIII, 354

  • Topics: Artificial Intelligence

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