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Selecting Models from Data

Artificial Intelligence and Statistics IV

  • P. Cheeseman
  • R. W. Oldford

Part of the Lecture Notes in Statistics book series (LNS, volume 89)

Table of contents

  1. Front Matter
    Pages i-x
  2. Overviews: Model Selection

  3. Graphical Methods

    1. Front Matter
      Pages 89-89
    2. David Madigan, Adrian E. Raftery, Jeremy C. York, Jeffrey M. Bradshaw, Russell G. Almond
      Pages 91-100
    3. Remco R. Bouckaert
      Pages 101-111
    4. Russell Almond, Jeffrey Bradshaw, David Madigan
      Pages 113-122
    5. David Madigan, Jeremy C. York, Jeffrey M. Bradshaw, Russell G. Almond
      Pages 123-131
    6. S. L. Lauritzen, B. Thiesson, D. J. Spiegelhalter
      Pages 143-152
    7. Nevin Lianwen Zhang, Runping Qi, David Poole
      Pages 163-172
    8. Raj Bhatnagar, Laveen N. Kanal
      Pages 173-179
  4. Causal Models

    1. Front Matter
      Pages 181-181
    2. Michael E. Sobel
      Pages 183-196
    3. Judea Pearl, Nanny Wermuth
      Pages 205-214
    4. Peter Spirtes, Clark Glymour
      Pages 215-222
    5. Sanjay Mishra, Prakash P. Shenoy
      Pages 223-232
    6. Floriana Esposito, Donato Malerba, Giovanni Semeraro
      Pages 233-242
    7. Paul R. Cohen, David M. Hart, Robert St. Amant, Lisa A. Ballesteros, Adam Carlson
      Pages 243-251
  5. Particular Models

    1. Front Matter
      Pages 253-253
    2. Randy Mechling, Marco Valtorta
      Pages 255-261
    3. Djamel Bouchaffra, Jacques Rouault
      Pages 263-271
    4. Scott D. Goodwin, Eric Neufeld, André Trudel
      Pages 273-282
    5. George J. Knafl, Andrej Semrl
      Pages 283-292
    6. D. Moreira dos Santos, R. B. Davies
      Pages 299-307
    7. Aaron Wallack, Edward Nicolson
      Pages 309-317
  6. Similarity-Based Models

    1. Front Matter
      Pages 319-319
    2. Carla E. Brodley, Paul E. Utgoff
      Pages 329-337
    3. Bradley L. Whitehall, David J. Sirag Jr.
      Pages 361-369
    4. C. Feng, R. King, A. Sutherland, S. Muggleton, R. Henery
      Pages 371-380
  7. Regression and Other Statistical Models

    1. Front Matter
      Pages 381-381
    2. Vladimir Cherkassky, Filip Mulier
      Pages 383-392
    3. Richard D. De Veaux, Lyle H. Ungar
      Pages 393-402
    4. Julian J. Faraway
      Pages 403-411
    5. D. G. Anglin, R. W. Oldford
      Pages 413-424
    6. Beat E. Neuenschwander, Bernard D. Flury
      Pages 425-432

About these proceedings

Introduction

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour­ aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.

Keywords

Hidden Markov Model artificial intelligence autonomous agents best fit calculus databases decision tree expert system knowledge knowledge-based system learning modeling neural network statistical model statistics

Editors and affiliations

  • P. Cheeseman
    • 1
  • R. W. Oldford
    • 2
  1. 1.Ames Research CenterNASAMoffet FieldUSA
  2. 2.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterloo OntarioCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-2660-4
  • Copyright Information Springer-Verlag New York 1994
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94281-0
  • Online ISBN 978-1-4612-2660-4
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site