Skip to main content
Springer Nature Link
Log in

Computational Learning Theory

Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Shai Ben-David
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1208)

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

Included in the following conference series:

Conference proceedings info: EuroCOLT 1997.

  • 3141 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, Israel, in March 1997.
The book presents 25 revised full papers carefully selected from a total of 36 high-quality submissions. The volume spans the whole spectrum of computational learning theory, with a certain emphasis on mathematical models of machine learning. Among the topics addressed are machine learning, neural nets, statistics, inductive inference, computational complexity, information theory, and theoretical physics.

Similar content being viewed by others

Keywords

Table of contents (26 papers)

  1. Front Matter

    Download chapter PDF

  2. Sample compression, learnability, and the Vapnik-Chervonenkis dimension

    • Manfred Warmuth
    Pages 1-2

  3. Learning boxes in high dimension

    • Amos Beimel, Eyal Kushilevitz
    Pages 3-15

  4. Learning monotone term decision lists

    • David Guijarro, Víctor Lavín, Vijay Raghavan
    Pages 16-26

  5. Learning matrix functions over rings

    • Nader H. Bshouty, Christino Tamon, David K. Wilson
    Pages 27-37

  6. Learning from incomplete boundary queries using split graphs and hypergraphs

    • Robert H. Sloan, György Turán
    Pages 38-50

  7. Generalization of the PAC-model for learning with partial information

    • Joel Ratsaby, Vitaly Maiorov
    Pages 51-65

  8. Monotonic and dual-monotonic probabilistic language learning of indexed families with high probability

    • Léa Meyer
    Pages 66-78

  9. Closedness properties in team learning of recursive functions

    • Juris Smotrovs
    Pages 79-93

  10. Structural measures for games and process control in the branch learning model

    • Matthias Ott, Frank Stephan
    Pages 94-108

  11. Learning under persistent drift

    • Yoav Freund, Yishay Mansour
    Pages 109-118

  12. Randomized hypotheses and minimum disagreement hypotheses for learning with noise

    • Nicolò Cesa-Bianchi, Paul Fischer, Eli Shamir, Hans Ulrich Simon
    Pages 119-133

  13. Learning when to trust which experts

    • David Helmbold, Stephen Kwek, Leonard Pitt
    Pages 134-149

  14. On learning branching programs and small depth circuits

    • Francesco Bergadano, Nader H. Bshouty, Christino Tamon, Stefano Varricchio
    Pages 150-161

  15. Learning nearly monotone k-term DNF

    • Jorge Castro, David Guijarro, Víctor Lavín
    Pages 162-170

  16. Optimal attribute-efficient learning of disjunction, parity, and threshold functions

    • Ryuhei Uehara, Kensei Tsuchida, Ingo Wegener
    Pages 171-184

  17. learning pattern languages using queries

    • Satoshi Matsumoto, Ayumi Shinohara
    Pages 185-197

  18. On fast and simple algorithms for finding Maximal subarrays and applications in learning theory

    • Andreas Birkendorf
    Pages 198-209

  19. A minimax lower bound for empirical quantizer design

    • Peter Bartlett, Tamás Linder, Gábor Lugosi
    Pages 210-222

  20. Vapnik-Chervonenkis dimension of recurrent neural networks

    • Pascal Koiran, Eduardo D. Sontag
    Pages 223-237
Back to top

Bibliographic Information

Publish with us

Policies and ethics

Back to top