Algorithmic Learning Theory

15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings

  • Shoham Ben-David
  • John Case
  • Akira Maruoka
Conference proceedings ALT 2004

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3244)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3244)

Table of contents

  1. Front Matter
  2. Invited Papers

    1. Ayumi Shinohara
      Pages 1-13
    2. Luc De Raedt, Kristian Kersting
      Pages 19-36
    3. Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pasi Rastas, Esko Ukkonen
      Pages 37-52
  3. Regular Contributions

    1. Inductive Inference

    2. PAC Learning and Boosting

      1. Kohei Hatano, Osamu Watanabe
        Pages 114-126
      2. Eiji Takimoto, Syuhei Koya, Akira Maruoka
        Pages 127-141
      3. Akinobu Miyata, Jun Tarui, Etsuji Tomita
        Pages 142-155
    3. Statistical Supervised Learning

    4. Statistical Analysis of Unlabeled Data

      1. Maria-Florina Balcan, Avrim Blum, Santosh Vempala
        Pages 194-205
      2. Kazuho Watanabe, Sumio Watanabe
        Pages 206-220
      3. Victor Maslov, Vladimir V’yugin
        Pages 221-233
      4. Marcus Hutter, Andrej Muchnik
        Pages 234-248
    5. Online Sequence Prediction

      1. Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin
        Pages 249-263
      2. Chamy Allenberg-Neeman, Benny Neeman
        Pages 264-278
    6. Approximate Optimization Algorithms

    7. Logic Based Learning

      1. Andrei Bulatov, Hubie Chen, Víctor Dalmau
        Pages 365-379
      2. Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán
        Pages 395-409
      3. Marta Arias, Roni Khardon
        Pages 410-424
    8. Query and Reinforcement Learning

      1. Satoshi Matsumoto, Takayoshi Shoudai
        Pages 425-439
      2. Jérôme Besombes, Jean-Yves Marion
        Pages 440-453
      3. Ana Iglesias, Paloma Martínez, Ricardo Aler, Fernando Fernández
        Pages 454-463
  4. Tutorial Papers

  5. Back Matter

About these proceedings


Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.


Boosting algorithmic learning theory algorithms learning learning theory logic optimization reinforcement learning supervised learning

Editors and affiliations

  • Shoham Ben-David
    • 1
  • John Case
    • 2
  • Akira Maruoka
    • 3
  1. 1.David R. Cheriton School of Computer Science University of Waterloo 
  2. 2.Department of Computer & Information SciencesUniversity of DelawareNewark
  3. 3.Dept. of Information Technology and ElectronicsIshinomaki Senshu University 

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-23356-5
  • Online ISBN 978-3-540-30215-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site