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  • Conference proceedings
  • © 2004

Algorithmic Learning Theory

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

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

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

Conference series link(s): ALT: International Conference on Algorithmic Learning Theory

Conference proceedings info: ALT 2004.

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Table of contents (37 papers)

  1. Front Matter

  2. Invited Papers

    1. String Pattern Discovery

      • Ayumi Shinohara
      Pages 1-13
    2. Probabilistic Inductive Logic Programming

      • Luc De Raedt, Kristian Kersting
      Pages 19-36
    3. Hidden Markov Modelling Techniques for Haplotype Analysis

      • Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pasi Rastas, Esko Ukkonen
      Pages 37-52
  3. Regular Contributions

    1. PAC Learning and Boosting

      1. Learning r-of-k Functions by Boosting
        • Kohei Hatano, Osamu Watanabe
        Pages 114-126
      2. Boosting Based on Divide and Merge
        • Eiji Takimoto, Syuhei Koya, Akira Maruoka
        Pages 127-141
      3. Learning Boolean Functions in AC 0 on Attribute and Classification Noise
        • Akinobu Miyata, Jun Tarui, Etsuji Tomita
        Pages 142-155
    2. Statistical Supervised Learning

      1. Complexity of Pattern Classes and Lipschitz Property
        • Amiran Ambroladze, John Shawe-Taylor
        Pages 181-193
    3. Statistical Analysis of Unlabeled Data

      1. On Kernels, Margins, and Low-Dimensional Mappings
        • Maria-Florina Balcan, Avrim Blum, Santosh Vempala
        Pages 194-205
      2. Estimation of the Data Region Using Extreme-Value Distributions
        • Kazuho Watanabe, Sumio Watanabe
        Pages 206-220
      3. Maximum Entropy Principle in Non-ordered Setting
        • Victor Maslov, Vladimir V’yugin
        Pages 221-233
      4. Universal Convergence of Semimeasures on Individual Random Sequences
        • Marcus Hutter, Andrej Muchnik
        Pages 234-248

Other Volumes

  1. Algorithmic Learning Theory

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

About this book

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.

Keywords

  • Boosting
  • algorithmic learning theory
  • algorithms
  • learning
  • learning theory
  • logic
  • optimization
  • reinforcement learning
  • supervised learning
  • algorithm analysis and problem complexity

Editors and Affiliations

  • David R. Cheriton School of Computer Science University of Waterloo,  

    Shoham Ben-David

  • Department of Computer & Information Sciences, University of Delaware, Newark

    John Case

  • Dept. of Information Technology and Electronics, Ishinomaki Senshu University,  

    Akira Maruoka

Bibliographic Information

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-30215-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 139.00
Price excludes VAT (USA)