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Algorithmic Learning Theory

12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001. Proceedings.

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. Naoki Abe

    1. IBM, Thomas J.Watson Research Center, Yorktown, USA

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  2. Roni Khardon

    1. Dept.of Electrical Engineering and Computer Science, Tufts University, Medford, USA

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  3. Thomas Zeugmann

    1. Medizinische Universität zu Lübeck,Inst.für Theoretische Informatik, Lübeck, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2225)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (27 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. Editors’ Introduction

    1. Editors’ Introduction

      • Naoki Abe, Roni Khardon, Thomas Zeugmann
      Pages 1-7

  3. Invited Papers

    1. The Discovery Science Project in Japan

      • Setsuo Arikawa
      Pages 9-11

    2. Queries Revisited

      • Dana Angluin
      Pages 12-31

    3. Robot Baby 2001

      • Paul R. Cohen, Tim Oates, Niall Adams, Carole R. Beal
      Pages 32-56

    4. Discovering Mechanisms: A Computational Philosophy of Science Perspective

      • Lindley Darden
      Pages 57-57

    5. Inventing Discovery Tools: Combining Information Visualization with Data Mining

      • Ben Shneiderman
      Pages 58-58

  5. Support Vector Machines

    1. Learning of Boolean Functions Using Support Vector Machines

      • Ken Sadohara
      Pages 106-118

    2. A Random Sampling Technique for Training Support Vector Machines

      • Jose Balcázar, Yang Dai, Osamu Watanabe
      Pages 119-134

  6. New Learning Models

    1. Learning Coherent Concepts

      • Ashutosh Garg, Dan Roth
      Pages 135-150

    2. Learning Intermediate Concepts

      • Stephen S. Kwek
      Pages 151-166

    3. Real-Valued Multiple-Instance Learning with Queries

      • Daniel R. Dooly, Sally A. Goldman, Stephen S. Kwek
      Pages 167-180

  7. Online Learning

    1. Loss Functions, Complexities, and the Legendre Transformation

      • Yuri Kalnishkan, Michael V. Vyugin, Volodya Vovk
      Pages 181-189

    2. Non-linear Inequalities between Predictive and Kolmogorov Complexities

      • Michael V. Vyugin, Vladimir V. V'yugin
      Pages 190-204

  8. Inductive Inference

    1. Learning by Switching Type of Information

      • Sanjay Jain, Frank Stephan
      Pages 205-218

    2. Learning How to Separate

      • Sanjay Jain, Frank Stephan
      Pages 219-234

    3. Learning Languages in a Union

      • Sanjay Jain, Yen Kaow Ng, Tiong Seng Tay
      Pages 235-250
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Keywords

About this book

This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).

Editors and Affiliations

  • IBM, Thomas J.Watson Research Center, Yorktown, USA

    Naoki Abe

  • Dept.of Electrical Engineering and Computer Science, Tufts University, Medford, USA

    Roni Khardon

  • Medizinische Universität zu Lübeck,Inst.für Theoretische Informatik, Lübeck, Germany

    Thomas Zeugmann

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