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Innovations in Machine Learning

Theory and Applications

  • Book
  • © 2006

Overview

Editors:
  1. Dawn E. Holmes

    1. Department of Statistics and Applied Probability, University of California at Santa Barbara, South Hall, Santa Barbara, USA

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  2. Lakhmi C. Jain

    1. School of Electrical & Information Engineering, Knowledge-Based Intelligent Engineering, Adelaide, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Latest research in machine learning

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 194)

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Table of contents (9 chapters)

  1. Front Matter

    Pages I-XVI
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  2. A Bayesian Approach to Causal Discovery

    • David Heckerman, Christopher Meek, Gregory Cooper
    Pages 1-28

  3. A Tutorial on Learning Causal Influence

    • Richard E. Neapolitan, Xia Jiang
    Pages 29-71

  4. Learning Based Programming

    • Dan Roth
    Pages 73-95

  5. N-1 Experiments Suffice to Determine the Causal Relations Among N Variables

    • Frederick Eberhardt, Clark Glymour, Richard Scheines
    Pages 97-112

  6. Support Vector Inductive Logic Programming

    • S. H. Muggleton, H. Lodhi, A. Amini, M. J. E. Sternberg
    Pages 113-135

  7. Neural Probabilistic Language Models

    • Yoshua Bengio, Holger Schwenk, Jean-Sébastien Senécal, Fréderic Morin, Jean-Luc Gauvain
    Pages 137-186

  8. Computational Grammatical Inference

    • Pieter W. Adriaans, Menno M. van Zaanen
    Pages 187-203

  9. On Kernel Target Alignment

    • Nello Cristianini, Jaz Kandola, Andre Elisseeff, John Shawe-Taylor
    Pages 205-256

  10. The Structure of Version Space

    • Ralf Herbrich, Thore Graepel, Robert C. Williamson
    Pages 257-273

  11. Back Matter

    Pages 275-276
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Keywords

About this book

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained.

Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

Editors and Affiliations

  • Department of Statistics and Applied Probability, University of California at Santa Barbara, South Hall, Santa Barbara, USA

    Dawn E. Holmes

  • School of Electrical & Information Engineering, Knowledge-Based Intelligent Engineering, Adelaide, Australia

    Lakhmi C. Jain

Bibliographic Information

  • Book Title: Innovations in Machine Learning

  • Book Subtitle: Theory and Applications

  • Editors: Dawn E. Holmes, Lakhmi C. Jain

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/3-540-33486-6

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2006

  • Hardcover ISBN: 978-3-540-30609-2Published: 09 March 2006

  • Softcover ISBN: 978-3-642-06788-4Published: 23 November 2010

  • eBook ISBN: 978-3-540-33486-6Published: 28 February 2006

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XVI, 276

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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