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Algorithmic Learning Theory

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

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Marcus Hutter

    1. RSISE @ ANU and SML @ NICTA, Canberra,, Australia

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  2. Rocco A. Servedio

    1. Columbia University, New York, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Eiji Takimoto

    1. Graduate School of Information Sciences, Tohoku University,, Japan

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    You can also search for this editor in PubMed   Google Scholar

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

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

Included in the following conference series:

Conference proceedings info: ALT 2007.

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Table of contents (31 papers)

  1. Front Matter

    Download chapter PDF

  2. Editors’ Introduction

    1. Editors’ Introduction

      • Marcus Hutter, Rocco A. Servedio, Eiji Takimoto
      Pages 1-8

  3. Invited Papers

    1. A Theory of Similarity Functions for Learning and Clustering

      • Avrim Blum
      Pages 9-9

    2. Machine Learning in Ecosystem Informatics

      • Thomas G. Dietterich
      Pages 10-11

    3. Challenge for Info-plosion

      • Masaru Kitsuregawa
      Pages 12-12

    4. A Hilbert Space Embedding for Distributions

      • Alex Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
      Pages 13-31

    5. Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity

      • Jürgen Schmidhuber
      Pages 32-33

  4. Invited Papers

    1. Inductive Inference

      1. Feasible Iteration of Feasible Learning Functionals
        • John Case, Timo Kötzing, Todd Paddock
        Pages 34-48
      2. Parallelism Increases Iterative Learning Power
        • John Case, Samuel E. Moelius III
        Pages 49-63
      3. Prescribed Learning of R.E. Classes
        • Sanjay Jain, Frank Stephan, Nan Ye
        Pages 64-78
      4. Learning in Friedberg Numberings
        • Sanjay Jain, Frank Stephan
        Pages 79-93

  5. Complexity Aspects of Learning

    1. Separating Models of Learning with Faulty Teachers

      • Vitaly Feldman, Shrenik Shah, Neal Wadhwa
      Pages 94-106

    2. Vapnik-Chervonenkis Dimension of Parallel Arithmetic Computations

      • César L. Alonso, José Luis Montaña
      Pages 107-119

    3. Parameterized Learnability of k-Juntas and Related Problems

      • Vikraman Arvind, Johannes Köbler, Wolfgang Lindner
      Pages 120-134

    4. On Universal Transfer Learning

      • M. M. Hassan Mahmud
      Pages 135-149

  6. Online Learning

    1. Tuning Bandit Algorithms in Stochastic Environments

      • Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári
      Pages 150-165

    2. Following the Perturbed Leader to Gamble at Multi-armed Bandits

      • Jussi Kujala, Tapio Elomaa
      Pages 166-180

    3. Online Regression Competitive with Changing Predictors

      • Steven Busuttil, Yuri Kalnishkan
      Pages 181-195

  7. Unsupervised Learning

    1. Cluster Identification in Nearest-Neighbor Graphs

      • Markus Maier, Matthias Hein, Ulrike von Luxburg
      Pages 196-210

    2. Multiple Pass Streaming Algorithms for Learning Mixtures of Distributions in \({\mathbb R}^d\)

      • Kevin L. Chang
      Pages 211-226
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  1. Algorithmic Learning Theory

Keywords

About this book

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.

Editors and Affiliations

  • RSISE @ ANU and SML @ NICTA, Canberra,, Australia

    Marcus Hutter

  • Columbia University, New York, USA

    Rocco A. Servedio

  • Graduate School of Information Sciences, Tohoku University,, Japan

    Eiji Takimoto

Bibliographic Information

  • Book Title: Algorithmic Learning Theory

  • Book Subtitle: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings

  • Editors: Marcus Hutter, Rocco A. Servedio, Eiji Takimoto

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-540-75225-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2007

  • Softcover ISBN: 978-3-540-75224-0Published: 17 September 2007

  • eBook ISBN: 978-3-540-75225-7Published: 11 October 2007

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 406

  • Topics: Artificial Intelligence, Data Mining and Knowledge Discovery

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