Algorithmic Learning Theory

18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007. Proceedings

  • Marcus Hutter
  • Rocco A. Servedio
  • Eiji Takimoto
Conference proceedings ALT 2007

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4754)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4754)

Table of contents

  1. Front Matter
  2. Editors’ Introduction

    1. Marcus Hutter, Rocco A. Servedio, Eiji Takimoto
      Pages 1-8
  3. Invited Papers

  4. Invited Papers

    1. Inductive Inference

      1. John Case, Timo Kötzing, Todd Paddock
        Pages 34-48
      2. John Case, Samuel E. Moelius III
        Pages 49-63
      3. Sanjay Jain, Frank Stephan, Nan Ye
        Pages 64-78
      4. Sanjay Jain, Frank Stephan
        Pages 79-93
  5. Complexity Aspects of Learning

    1. Vitaly Feldman, Shrenik Shah, Neal Wadhwa
      Pages 94-106
    2. César L. Alonso, José Luis Montaña
      Pages 107-119
    3. Vikraman Arvind, Johannes Köbler, Wolfgang Lindner
      Pages 120-134
    4. M. M. Hassan Mahmud
      Pages 135-149
  6. Online Learning

    1. Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári
      Pages 150-165
    2. Jussi Kujala, Tapio Elomaa
      Pages 166-180
    3. Steven Busuttil, Yuri Kalnishkan
      Pages 181-195
  7. Unsupervised Learning

About these proceedings

Introduction

This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1–4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. The conference was co-located with the Tenth International Conference on Discovery Science (DS 2007). This volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ?ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. These invited talks were presented to the audience of both conferences in joint sessions.

Keywords

Boosting Support Vector Machine algorithmic learning theory algorithms complexity kernel method learning learning theory machine learning reinforcement learning supervised learning unsupervised learning

Editors and affiliations

  • Marcus Hutter
    • 1
  • Rocco A. Servedio
    • 2
  • Eiji Takimoto
    • 3
  1. 1.RSISE @ ANU and SML @ NICTA, Canberra,Australia
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.Graduate School of Information SciencesTohoku University,Japan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-75225-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-75224-0
  • Online ISBN 978-3-540-75225-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book