© 2001

Algorithmic Learning Theory

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

  • Naoki Abe
  • Roni Khardon
  • Thomas Zeugmann
Conference proceedings ALT 2001

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

Also part of the Lecture Notes in Computer Science book sub series (LNAI, volume 2225)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Editors’ Introduction

    1. Naoki Abe, Roni Khardon, Thomas Zeugmann
      Pages 1-7
  3. Invited Papers

    1. Setsuo Arikawa
      Pages 9-11
    2. Dana Angluin
      Pages 12-31
    3. Paul R. Cohen, Tim Oates, Niall Adams, Carole R. Beal
      Pages 32-56
  4. Complexity of Learning

  5. Support Vector Machines

    1. Jose Balcázar, Yang Dai, Osamu Watanabe
      Pages 119-134
  6. New Learning Models

    1. Ashutosh Garg, Dan Roth
      Pages 135-150
    2. Stephen S. Kwek
      Pages 151-166
    3. Daniel R. Dooly, Sally A. Goldman, Stephen S. Kwek
      Pages 167-180
  7. Online Learning

    1. Yuri Kalnishkan, Michael V. Vyugin, Volodya Vovk
      Pages 181-189
    2. Michael V. Vyugin, Vladimir V. V'yugin
      Pages 190-204
  8. Inductive Inference

    1. Sanjay Jain, Frank Stephan
      Pages 205-218
    2. Sanjay Jain, Frank Stephan
      Pages 219-234
    3. Sanjay Jain, Yen Kaow Ng, Tiong Seng Tay
      Pages 235-250

About these proceedings


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).


Algorithmic Learning Computational Learning Concept Learning Discovery Science Inductive Inference Learning Algorithms Machine Learning Neural Network Learning Support Vector Machine Support Vector Machines algorithmic learning theory algorithms complexity learning learning theory

Editors and affiliations

  • Naoki Abe
    • 1
  • Roni Khardon
    • 2
  • Thomas Zeugmann
    • 3
  1. 1.IBM, Thomas J.Watson Research CenterYorktownUSA
  2. 2.Dept.of Electrical Engineering and Computer ScienceTufts UniversityMedfordUSA
  3. 3.Medizinische Universität zu Lübeck,Inst.für Theoretische InformatikLübeckGermany

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