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Algorithmic Learning Theory

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

  • José L. Balcázar
  • Philip M. Long
  • Frank Stephan
Conference proceedings ALT 2006

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

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4264)

Table of contents

  1. Front Matter
    PDF

  2. Editors’ Introduction

    1. Editors’ Introduction
      Jose L. Balcázar, Philip M. Long, Frank Stephan
      Pages 1-9

  3. Invited Contributions

    1. Solving Semi-infinite Linear Programs Using Boosting-Like Methods
      Gunnar Rätsch
      Pages 10-11
    2. e-Science and the Semantic Web: A Symbiotic Relationship
      Carole Goble, Oscar Corcho, Pinar Alper, David De Roure
      Pages 12-12
    3. Spectral Norm in Learning Theory: Some Selected Topics
      Hans Ulrich Simon
      Pages 13-27
    4. Data-Driven Discovery Using Probabilistic Hidden Variable Models
      Padhraic Smyth
      Pages 28-28
    5. Reinforcement Learning and Apprenticeship Learning for Robotic Control
      Andrew Y. Ng
      Pages 29-31

  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
    14. Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring
      Chamy Allenberg, Peter Auer, László Györfi, György Ottucsák
      Pages 229-243
    15. General Discounting Versus Average Reward
      Marcus Hutter
      Pages 244-258
    16. The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection
      Jan Poland
      Pages 259-273
    17. Is There an Elegant Universal Theory of Prediction?
      Shane Legg
      Pages 274-287
    18. Learning Linearly Separable Languages
      Leonid Kontorovich, Corinna Cortes, Mehryar Mohri
      Pages 288-303
    19. Smooth Boosting Using an Information-Based Criterion
      Kohei Hatano
      Pages 304-318
    20. Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
      Hsuan-Tien Lin, Ling Li
      Pages 319-333
    21. Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence
      Daniil Ryabko, Marcus Hutter
      Pages 334-347
    22. Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning
      Takeshi Shibata, Ryo Yoshinaka, Takashi Chikayama
      Pages 348-362
    23. Unsupervised Slow Subspace-Learning from Stationary Processes
      Andreas Maurer
      Pages 363-377
    24. Learning-Related Complexity of Linear Ranking Functions
      Atsuyoshi Nakamura
      Pages 378-392
  5. Back Matter
    PDF

About these proceedings

Keywords

Boosting Support Vector Machine algorithm algorithmic learning theory algorithms kernel method learning learning theory machine learning reinforcement learning

Editors and affiliations

  • José L. Balcázar
    • 1
  • Philip M. Long
    • 2
  • Frank Stephan
    • 3
  1. 1.Departament de Llenguatges i Sistemes Informàtics Laboratori d’Algorísmica Relacional, Complexitat i AprenentatgeUniversitat Politècnica de CatalunyaBarcelona 
  2. 2.GoogleMountain ViewUSA
  3. 3.Department of Computer Science and Department of MathematicsNational University of SingaporeSingaporeRepublic of Singapore

Bibliographic information

  • DOI https://doi.org/10.1007/11894841
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-46649-9
  • Online ISBN 978-3-540-46650-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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