Skip to main content
SpringerLink
Log in

Machine Learning: From Theory to Applications

Cooperative Research at Siemens and MIT

  • Book
  • © 1993

Overview

Editors:
  1. Stephen José Hanson
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Werner Remmele
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Ronald L. Rivest
    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 661)

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

Table of contents (17 chapters)

  1. Front Matter

    Download chapter PDF

  2. Strategic directions in machine learning

    • Stephen José Hanson, Werner Remmele, Ronald L. Rivest
    Pages 1-4

  3. Introduction

    • Ronald L. Rivest
    Pages 5-7

  4. Training a 3-node neural network is NP-complete

    • Avrim L. Blum, Ronald L. Rivest
    Pages 9-28

  5. Cryptographic limitations on learning Boolean formulae and finite automata

    • Michael J. Kearns, Leslie G. Valiant
    Pages 29-49

  6. Inference of finite automata using homing sequences

    • Ronald L. Rivest, Robert E. Schapire
    Pages 51-73

  7. Introduction

    • Werner Remmele
    Pages 75-77

  8. Adaptive search by learning from incomplete explanations of failures

    • Neeraj Bhatnagar
    Pages 79-92

  9. Learning of rules for fault diagnosis in power supply networks

    • R. Meunier, R. Scheiterer, A. Hecht
    Pages 93-105

  10. Cross references are features

    • Robert W. Schwanke, Michael A. Platoff
    Pages 107-123

  11. The schema mechanism

    • Gary L. Drescher
    Pages 125-138

  12. L-ATMS: A tight integration of EBL and the ATMS

    • Kai Zercher
    Pages 139-152

  13. Introduction

    • Stephen Josè Hanson
    Pages 153-156

  14. Massively parallel symbolic induction of protein structure/function relationships

    • Richard H. Lathrop, Teresa A. Webster, Temple F. Smith, Patrick H. Winston
    Pages 157-173

  15. Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks

    • Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto
    Pages 175-202

  16. Phoneme discrimination using connectionist networks

    • Raymond L. Watrous
    Pages 203-227

  17. Behavior-based learning to control IR oven heating: Preliminary investigations

    • R. Chou, P. Liu, J. Vallino, M. Y. Chiu
    Pages 229-240

  18. Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks

    • Thomas Petsche, Bradley W. Dickinson
    Pages 241-268

  19. Back Matter

    Download chapter PDF
Back to top

Keywords

About this book

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

Bibliographic Information

  • Book Title: Machine Learning: From Theory to Applications

  • Book Subtitle: Cooperative Research at Siemens and MIT

  • Editors: Stephen José Hanson, Werner Remmele, Ronald L. Rivest

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-56483-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1993

  • Softcover ISBN: 978-3-540-56483-6Published: 30 March 1993

  • eBook ISBN: 978-3-540-47568-2Published: 02 July 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: VIII, 276

  • Topics: Artificial Intelligence, Computation by Abstract Devices, Processor Architectures

Publish with us

Policies and ethics

Back to top

Search

Navigation