Algorithmic Learning Theory

1. Front Matter

   Pages I-XI

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2. Editors’ Introduction

   1. Editors’ Introduction

      Pages 1-6

3. Invited Papers

   1. Mathematics Based on Learning

      • Susumu Hayashi

      Pages 7-21

   2. Data Mining with Graphical Models

      • Rudolf Kruse, Christian Borgelt

      Pages 22-22

   3. On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum

      • John Shawe-Taylor, Chris Williams, Nello Cristianini, Jaz Kandola

      Pages 23-40

   4. In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project

      • Gerhard Widmer

      Pages 41-41

   5. Learning Structure from Sequences, with Applications in a Digital Library

      • Ian H. Witten

      Pages 42-56

4. Regular Contributions

   1. Learning Boolean Functions

      1. On Learning Monotone Boolean Functions under the Uniform Distribution

         • Kazuyuki Amano, Akira Maruoka

         Pages 57-68

      2. On Learning Embedded Midbit Functions

         • Rocco A. Servedio

         Pages 69-82

      3. Maximizing Agreements and CoAgnostic Learning

         • Nader H. Bshouty, Lynn Burroughs

         Pages 83-97

   2. Boosting and Margin-Based Learning

      1. Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning

         • Dmitry Gavinsky

         Pages 98-112

      2. Large Margin Classification for Moving Targets

         • Jyrki Kivinen, Alex J. Smola, Robert C. Williamson

         Pages 113-127

      3. On the Smallest Possible Dimension and the Largest Possible Margin of Linear Arrangements Representing Given Concept Classes Uniform Distribution

         • Jürgen Forster, Hans Ulrich Simon

         Pages 128-138

   3. Learning with Queries

      1. A General Dimension for Approximately Learning Boolean Functions

         • Johannes Köbler, Wolfgang Lindner

         Pages 139-148

      2. The Complexity of Learning Concept Classes with Polynomial General Dimension

         • Johannes Köbler, Wolfgang Lindner

         Pages 149-163

      3. On the Absence of Predictive Complexity for Some Games

         • Yuri Kalnishkan, Michael V. Vyugin

         Pages 164-172

   4. Learning and Information Extraction

      1. Consistency Queries in Information Extraction

         • Gunter Grieser, Klaus P. Jantke, Steffen Lange

         Pages 173-187

      2. Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Data

         • Yusuke Suzuki, Takayoshi Shoudai, Tomoyuki Uchida, Tetsuhiro Miyahara

         Pages 188-202

   5. Inductive Inference

      1. Reflective Inductive Inference of Recursive Functions

         • Gunter Grieser

         Pages 203-217

      2. Classes with Easily Learnable Subclasses

         • Sanjay Jain, Wolfram Menzel, Frank Stephan

         Pages 218-232

This volume contains the papers presented at the 13th Annual Conference on Algorithmic Learning Theory (ALT 2002), which was held in Lub ¨ eck (Germany) during November 24–26, 2002. The main objective of the conference was to p- vide an interdisciplinary forum discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was colocated with the Fifth International Conference on Discovery Science (DS 2002). The volume includes 26 technical contributions which were selected by the program committee from 49 submissions. It also contains the ALT 2002 invited talks presented by Susumu Hayashi (Kobe University, Japan) on “Mathematics Based on Learning”, by John Shawe-Taylor (Royal Holloway University of L- don, UK) on “On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum”, and by Ian H. Witten (University of Waikato, New Zealand) on “Learning Structure from Sequences, with Applications in a Digital Library” (joint invited talk with DS 2002). Furthermore, this volume - cludes abstracts of the invited talks for DS 2002 presented by Gerhard Widmer (Austrian Research Institute for Arti?cial Intelligence, Vienna) on “In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project” and by Rudolf Kruse (University of Magdeburg, Germany) on “Data Mining with Graphical Models”. The complete versions of these papers are published in the DS 2002 proceedings (Lecture Notes in Arti?cial Intelligence, Vol. 2534). ALT has been awarding the E.

• Boolean function
• algorithms
• heuristics
• learning
• learning theory
• logic
• programming
• algorithm analysis and problem complexity

• Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Crema (CR), Italy

  Nicolò Cesa-Bianchi

• Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan

  Masayuki Numao

• Institut für Theoretische Informatik, Universität zu Lübeck, Lübeck, Germany

  Rüdiger Reischuk

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