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Machine Learning

A Guide to Current Research

  • Book
  • © 1986

Overview

Authors:
  1. Tom M. Mitchell

    1. Rutgers University, USA

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  2. Jaime G. Carbonell

    1. Carnegie-Mellon University, USA

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  3. Ryszard S. Michalski

    1. University of Illinois, USA

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Table of contents (77 chapters)

  1. Front Matter

    Pages i-xv
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  2. Judge: A Case-based Reasoning System

    • William M. Bain
    Pages 1-4

  3. Changing Language while Learning Recursive Descriptions from Examples

    • Ranan B. Banerji
    Pages 5-9

  4. Learning by Disjunctive Spanning

    • Gary L. Bradshaw
    Pages 11-14

  5. Transfer of Knowledge Between Teaching and Learning Systems

    • P. Brazdil
    Pages 15-18

  6. Some Approaches to Knowledge Acquisition

    • Bruce G. Buchanan
    Pages 19-24

  7. Analogical Learning with Multiple Models

    • Mark H. Burstein
    Pages 25-28

  8. The World Modelers Project: Objectives and Simulator Architecture

    • Jaime Carbonell, Greg Hood
    Pages 29-34

  9. The Acquisition of Procedural Knowledge through Inductive Learning

    • Kaihu Chen
    Pages 35-38

  10. Learning Static Evaluation Functions by Linear Regression

    • Jens Christensen
    Pages 39-42

  11. Plan Invention and Plan Transformation

    • Gregg C. Collins
    Pages 43-45

  12. A Brief Overview of Explanatory Schema Acquisition

    • Gerald Dejong
    Pages 47-50

  13. The EG Project: Recent Progress

    • Thomas G. Dietterich
    Pages 51-54

  14. Learning Causal Relations

    • Richard J. Doyle
    Pages 55-58

  15. Functional Properties and Concept Formation

    • J. Daniel Easterlin
    Pages 59-62

  16. Explanation-Based Learning in Logic Circuit Design

    • Thomas Ellman
    Pages 63-66

  17. A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects

    • Douglas Fisher
    Pages 67-70

  18. Exploiting Functional Vocabularies to Learn Structural Descriptions

    • Nicholas S. Flann, Thomas G. Dietterich
    Pages 71-74

  19. Combining Numeric and Symbolic Learning Techniques

    • Richard H. Granger Jr., Jeffrey C. Schlimmer
    Pages 75-80

  20. Learning by Understanding Analogies

    • Russell Greiner
    Pages 81-84
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Keywords

About this book

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

Authors and Affiliations

  • Rutgers University, USA

    Tom M. Mitchell

  • Carnegie-Mellon University, USA

    Jaime G. Carbonell

  • University of Illinois, USA

    Ryszard S. Michalski

Bibliographic Information

  • Book Title: Machine Learning

  • Book Subtitle: A Guide to Current Research

  • Authors: Tom M. Mitchell, Jaime G. Carbonell, Ryszard S. Michalski

  • Series Title: The Springer International Series in Engineering and Computer Science

  • DOI: https://doi.org/10.1007/978-1-4613-2279-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Kluwer Academic Publishers 1986

  • Hardcover ISBN: 978-0-89838-214-3Published: 30 April 1986

  • Softcover ISBN: 978-1-4612-9406-1Published: 14 October 2011

  • eBook ISBN: 978-1-4613-2279-5Published: 06 December 2012

  • Series ISSN: 0893-3405

  • Edition Number: 1

  • Number of Pages: XVI, 429

  • Topics: Artificial Intelligence

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