Machine Learning

A Guide to Current Research

  • Tom M. Mitchell
  • Jaime G. Carbonell
  • Ryszard S. Michalski

Table of contents

  1. Front Matter
    Pages i-xv
  2. William M. Bain
    Pages 1-4
  3. Gary L. Bradshaw
    Pages 11-14
  4. Bruce G. Buchanan
    Pages 19-24
  5. Mark H. Burstein
    Pages 25-28
  6. Gregg C. Collins
    Pages 43-45
  7. Thomas G. Dietterich
    Pages 51-54
  8. Richard J. Doyle
    Pages 55-58
  9. J. Daniel Easterlin
    Pages 59-62
  10. Nicholas S. Flann, Thomas G. Dietterich
    Pages 71-74
  11. Richard H. Granger Jr., Jeffrey C. Schlimmer
    Pages 75-80
  12. Russell Greiner
    Pages 81-84
  13. Haym Hirsh, Derek Sleeman
    Pages 93-97
  14. Larry Hunter
    Pages 109-113
  15. Glenn A. Iba
    Pages 115-117
  16. Smadar Kedar-Cabelli
    Pages 123-126
  17. Kevin T. Kelly
    Pages 133-136
  18. Dennis Kibler, Rogers P. Hall
    Pages 137-140
  19. Yves Kodratoff
    Pages 145-150
  20. Janet L. Kolodner, Robert L. Simpson
    Pages 155-159
  21. Pat Langley, Dennis Kibler, Richard Granger
    Pages 167-171
  22. Robert W. Lawler
    Pages 173-177
  23. Alan J. MacDonald
    Pages 183-187
  24. Steven Minton
    Pages 199-202
  25. Tom M. Mitchell, Sridhar Mahadevan, Louis I. Steinberg
    Pages 203-206
  26. Jack Mostow
    Pages 213-218
  27. Michael C. Mozer, Klaus P. Gross
    Pages 219-226

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.


artificial intelligence case-based reasoning circuit design classification heuristics intelligence learning logical reasoning machine learning robot

Authors and affiliations

  • Tom M. Mitchell
    • 1
  • Jaime G. Carbonell
    • 2
  • Ryszard S. Michalski
    • 3
  1. 1.Rutgers UniversityUSA
  2. 2.Carnegie-Mellon UniversityUSA
  3. 3.University of IllinoisUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag US 1986
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-9406-1
  • Online ISBN 978-1-4613-2279-5
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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