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Learning from Data

Artificial Intelligence and Statistics V

  • Doug Fisher
  • Hans-J. Lenz

Part of the Lecture Notes in Statistics book series (LNS, volume 112)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Causality

    1. Front Matter
      Pages 1-1
    2. Paul R. Cohen, Dawn E. Gregory, Lisa Ballesteros, Robert St. Amant
      Pages 3-12
    3. Qing Yao, David Tritchler
      Pages 35-44
  3. Inference and Decision Making

    1. Front Matter
      Pages 45-45
    2. Alberto Lekuona, Beatriz Lacruz, Pilar Lasala
      Pages 69-77
    3. David Madigan, Russell G. Almond
      Pages 89-98
    4. Xiaorong Sun, Steve Y. Chiu, Louis Anthony Cox
      Pages 109-117
  4. Search Control in Model Hunting

    1. Front Matter
      Pages 119-119
    2. David Maxwell Chickering
      Pages 121-130
    3. Jörg Gebhardt, Rudolf Kruse
      Pages 143-153
    4. Pedro Larrañaga, Roberto Murga, Mikel Poza, Cindy Kuijpers
      Pages 165-174
    5. Tim Oates, Matthew D. Schmill, Dawn E. Gregory, Paul R. Cohen
      Pages 185-195
  5. Classification

    1. Front Matter
      Pages 197-197
    2. David W. Aha, Richard L. Bankert
      Pages 199-206
    3. Ruth Bergman, Ronald L. Rivest
      Pages 219-227
    4. A. Ketterlin, P. Gançarski, J. J. Korczak
      Pages 229-238
    5. Michael J. Pazzani
      Pages 239-248
  6. General Learning Issues

    1. Front Matter
      Pages 249-249
    2. A. Feelders, W. Verkooijen
      Pages 271-279
    3. Christopher J. Merz
      Pages 281-290
    4. Gregory M. Provan, Moninder Singh
      Pages 291-300
    5. Geetha Srikantan, Sargur N. Srihari
      Pages 301-310
  7. EDA: Tools and Methods

    1. Front Matter
      Pages 311-311
    2. Jason Catlett
      Pages 313-322
    3. Olivier de Vel, Sofianto Li, Danny Coomans
      Pages 323-331
    4. E. James Harner, Hanga C. Galfalvy
      Pages 333-342
    5. Pat Riddle, Roman Fresnedo, David Newman
      Pages 343-352
    6. Robert St. Amant, Paul R. Cohen
      Pages 353-362
  8. Decision and Regression Tree Induction

    1. Front Matter
      Pages 363-363
    2. Donato Malerba, Floriana Esposito, Giovanni Semeraro
      Pages 365-374
    3. George H. John
      Pages 375-385
    4. David Lubinsky
      Pages 387-398
  9. Natural Language Processing

    1. Front Matter
      Pages 411-411
    2. William DuMouchel, Carol Friedman, George Hripcsak, Stephen B. Johnson, Paul D. Clayton
      Pages 413-421
    3. Alexander Franz
      Pages 423-432
    4. Kaname Kasahara, Kazumitsu Matsuzawa, Tsutomu Ishikawa, Tsukasa Kawaoka
      Pages 433-442

About this book

Introduction

Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.

Keywords

Bayesian network Likelihood artificial intelligence control data analysis decision problem decision tree genetic algorithms intelligence knowledge knowledge discovery learning modeling natural language natural language processing

Editors and affiliations

  • Doug Fisher
    • 1
  • Hans-J. Lenz
    • 2
  1. 1.Department of Computer ScienceVanderbilt UniversityNashvilleUSA
  2. 2.Department of Economics Institute of Statistics and EconometricsFree University of BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-2404-4
  • Copyright Information Springer-Verlag New York 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94736-5
  • Online ISBN 978-1-4612-2404-4
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site