Algorithmic Learning Theory

9th International Conference, ALT’98 Otzenhausen, Germany, October 8–10, 1998 Proceedings

  • Michael M. Richter
  • Carl H. Smith
  • Rolf Wiehagen
  • Thomas Zeugmann
Conference proceedings ALT 1998
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1501)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Editors’ Introduction

    1. Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann
      Pages 1-10
  3. Inductive Logic Programming and Data Mining

  4. Inductive Inference

    1. John Case, Matthias Ott, Arun Sharma, Frank Stephan
      Pages 31-45
    2. Kalvis ApsĪtis, RŪsiņš Freivalds, Raimonds Simanovskis, Juris Smotrovs
      Pages 46-60
  5. Learning via Queries

  6. Prediction Algorithns

    1. Akira Maruoka, Eiji Takimoto
      Pages 127-142
  7. Inductive Logic Programming

  8. Learning Formal Languages

    1. Tom Head, Satoshi Kobayashi, Takashi Yokomori
      Pages 191-204
    2. John Case, Sanjay Jain
      Pages 205-219
    3. Masako Sato, Yasuhito Mukouchi, Dao Zheng
      Pages 220-233
    4. Hiroki Arimura, Atsushi Wataki, Ryoichi Fujino, Setsuo Arikawa
      Pages 247-261

About these proceedings

Introduction

This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ¨aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.

Keywords

Automat Boolean function Racter Variable algorithms automata complexity data mining databases formal language knowledge learning learning theory logic programming

Editors and affiliations

  • Michael M. Richter
    • 1
  • Carl H. Smith
    • 2
  • Rolf Wiehagen
    • 3
  • Thomas Zeugmann
    • 4
  1. 1.AG Künstliche Intelligenz - ExpertensystemeUniversitÄt KaiserslauternKaiserslauternGermany
  2. 2.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  3. 3.AG Algorithmischesn LernenUniversitÄt KaiserslauternKaiserslauternGermany
  4. 4.Graduate School of Information Science and Electrical Engineering Department of InformaticsKyushu UniversityKassugaJapan

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-49730-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 1998
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-65013-3
  • Online ISBN 978-3-540-49730-1
  • Series Print ISSN 0302-9743
  • About this book