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Algorithmic Learning Theory

17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. José L. Balcázar

    1. Departament de Llenguatges i Sistemes Informàtics Laboratori d’Algorísmica Relacional, Complexitat i Aprenentatge, Universitat Politècnica de Catalunya, Barcelona,  

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  2. Philip M. Long

    1. Google, Mountain View, USA

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  3. Frank Stephan

    1. Department of Computer Science and Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4264)

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

Included in the following conference series:

Conference proceedings info: ALT 2006.

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Keywords

Table of contents (30 papers)

  1. Front Matter

    Download chapter PDF

  2. Editors’ Introduction

    1. Editors’ Introduction

      • Jose L. Balcázar, Philip M. Long, Frank Stephan
      Pages 1-9

  4. Regular Contributions

    1. Learning Unions of ω(1)-Dimensional Rectangles

      • Alp Atıcı, Rocco A. Servedio
      Pages 32-47

    2. On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle

      • Nader H. Bshouty, Ehab Wattad
      Pages 48-62

    3. Active Learning in the Non-realizable Case

      • Matti Kääriäinen
      Pages 63-77

    4. How Many Query Superpositions Are Needed to Learn?

      • Jorge Castro
      Pages 78-92

    5. Teaching Memoryless Randomized Learners Without Feedback

      • Frank J. Balbach, Thomas Zeugmann
      Pages 93-108

    6. The Complexity of Learning SUBSEQ (A)

      • Stephen Fenner, William Gasarch
      Pages 109-123

    7. Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data

      • Matthew de Brecht, Akihiro Yamamoto
      Pages 124-138

    8. Learning and Extending Sublanguages

      • Sanjay Jain, Efim Kinber
      Pages 139-153

    9. Iterative Learning from Positive Data and Negative Counterexamples

      • Sanjay Jain, Efim Kinber
      Pages 154-168

    10. Towards a Better Understanding of Incremental Learning

      • Sanjay Jain, Steffen Lange, Sandra Zilles
      Pages 169-183

    11. On Exact Learning from Random Walk

      • Nader H. Bshouty, Iddo Bentov
      Pages 184-198

    12. Risk-Sensitive Online Learning

      • Eyal Even-Dar, Michael Kearns, Jennifer Wortman
      Pages 199-213

    13. Leading Strategies in Competitive On-Line Prediction

      • Vladimir Vovk
      Pages 214-228
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Other volumes

  1. Algorithmic Learning Theory

Editors and Affiliations

  • Departament de Llenguatges i Sistemes Informàtics Laboratori d’Algorísmica Relacional, Complexitat i Aprenentatge, Universitat Politècnica de Catalunya, Barcelona,  

    José L. Balcázar

  • Google, Mountain View, USA

    Philip M. Long

  • Department of Computer Science and Department of Mathematics, National University of Singapore, Singapore, Republic of Singapore

    Frank Stephan

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