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Algorithmic Learning for Knowledge-Based Systems

GOSLER Final Report

  • Book
  • © 1995

Overview

Editors:
  1. Klaus P. Jantke
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  2. Steffen Lange
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 961)

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

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Table of contents (23 chapters)

  1. Front Matter

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  2. Learning and consistency

    • Rolf Wiehagen, Thomas Zeugmann
    Pages 1-24

  3. Error detecting in inductive inference

    • Rūsiņš Freivalds, Efim B. Kinber, Rolf Wiehagen
    Pages 25-48

  4. Learning from good examples

    • Rūsiņš Freivalds, Efim B. Kinber, Rolf Wiehagen
    Pages 49-62

  5. Towards reduction arguments for FINite learning

    • Robert Daley, Bala Kalyanasundaram
    Pages 63-75

  6. Not-so-nearly-minimal-size program inference (preliminary report)

    • John Case, Mandayam Suraj, Sanjay Jain
    Pages 76-95

  7. Optimization problem in inductive inference

    • Andris Ambainis
    Pages 96-107

  8. On identification by teams and probabilistic machines

    • Sanjay Jain, Arun Sharma
    Pages 108-145

  9. Topological considerations in composing teams of learning machines

    • Kalvis Apsītis
    Pages 146-154

  10. Probabilistic versus deterministic memory limited learning

    • Rūsiņš Freivalds, Efim B. Kinber, Carl H. Smith
    Pages 155-161

  11. Classification using information

    • William I. Gasarch, Mark G. Pleszkoch, Mahendran Velauthapillai
    Pages 162-173

  12. Classifying recursive predicates and languages

    • Rolf Wiehagen, Carl H. Smith, Thomas Zeugmann
    Pages 174-189

  13. A guided tour across the boundaries of learning recursive languages

    • Thomas Zeugmann, Steffen Lange
    Pages 190-258

  14. Pattern inference

    • Takeshi Shinohara, Setsuo Arikawa
    Pages 259-291

  15. Inductive learning of recurrence-term languages from positive data

    • Phil Watson
    Pages 292-315

  16. Learning formal languages based on control sets

    • Yuji Takada
    Pages 316-339

  17. Learning in case-based classification algorithms

    • Christoph Globig, Stefan Wess
    Pages 340-362

  18. Optimal strategies — Learning from examples — Boolean equations

    • Christian Posthoff, Michael Schlosser
    Pages 363-390

  19. Feature construction during tree learning

    • Gerhard Mehlsam, Hermann Kaindl, Wilhelm Barth
    Pages 391-403

  20. On lower bounds for the depth of threshold circuits with weights from {−1,0,+1}

    • Andreas Albrecht
    Pages 404-416
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Keywords

About this book

This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation.
The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.

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