Algorithmic Learning Theory

19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings

  • Yoav Freund
  • László Györfi
  • György Turán
  • Thomas Zeugmann

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5254)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 5254)

Table of contents

  1. Front Matter
  2. Invited Papers

  3. Regular Contributions

    1. Statistical Learning

      1. Stéphan Clémençon, Nicolas Vayatis
        Pages 22-37
      2. Corinna Cortes, Mehryar Mohri, Michael Riley, Afshin Rostamizadeh
        Pages 38-53
      3. Andreas Maurer, Massimiliano Pontil
        Pages 70-78
      4. Andreas Maurer, Massimiliano Pontil
        Pages 79-91
      5. Ohad Shamir, Sivan Sabato, Naftali Tishby
        Pages 92-107
    2. Probability and Stochastic Processes

      1. László Györfi, István Vajda
        Pages 108-122
      2. Alexey Chernov, Alexander Shen, Nikolai Vereshchagin, Vladimir Vovk
        Pages 138-153
      3. Vladimir Vovk, Alexander Shen
        Pages 154-168
    3. Boosting and Experts

      1. Alexey Chernov, Yuri Kalnishkan, Fedor Zhdanov, Vladimir Vovk
        Pages 199-213

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008.

The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.

Keywords

Clustering comlexity concept spaces half-space learning language learning learning machine learning markov process neural representation visual datamining visualization

Editors and affiliations

  • Yoav Freund
    • 1
  • László Györfi
    • 2
  • György Turán
    • 3
  • Thomas Zeugmann
    • 4
  1. 1.Computer Science and EngineeringUniversity of CaliforniaSan DiegoUSA
  2. 2.Department of Computer Science and Information TheoryDepartment of Computer Science and Budapest University of Technology and EconomicsBudapestHungary
  3. 3.Department of Math., Stat. and Comp. Sci,University of IllinoisChicagoUSA
  4. 4.Division of Computer ScienceHokkaido UniversitySapporoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-87987-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-87986-2
  • Online ISBN 978-3-540-87987-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book