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Deterministic and Statistical Methods in Machine Learning

First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Joab Winkler

    1. Department of Computer Science, The University of Sheffield, Sheffield, UK

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  2. Mahesan Niranjan

    1. Department of Computer Science, The University of Sheffield, Sheffield, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. Neil Lawrence

    1. University of Manchester, UK

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3635)

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

Included in the following conference series:

Conference proceedings info: DSMML 2004.

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Table of contents (19 papers)

  1. Front Matter

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  2. Object Recognition via Local Patch Labelling

    • Christopher M. Bishop, Ilkay Ulusoy
    Pages 1-21

  3. Multi Channel Sequence Processing

    • Samy Bengio, Hervé Bourlard
    Pages 22-36

  4. Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis

    • Gavin C. Cawley, Nicola L. C. Talbot, Gareth J. Janacek, Michael W. Peck
    Pages 37-55

  5. Extensions of the Informative Vector Machine

    • Neil D. Lawrence, John C. Platt, Michael I. Jordan
    Pages 56-87

  6. Efficient Communication by Breathing

    • Tom H. Shorrock, David J. C. MacKay, Chris J. Ball
    Pages 88-97

  7. Guiding Local Regression Using Visualisation

    • Dharmesh M. Maniyar, Ian T. Nabney
    Pages 98-109

  8. Transformations of Gaussian Process Priors

    • Roderick Murray-Smith, Barak A. Pearlmutter
    Pages 110-123

  9. Kernel Based Learning Methods: Regularization Networks and RBF Networks

    • Petra Kudová, Roman Neruda
    Pages 124-136

  10. Redundant Bit Vectors for Quickly Searching High-Dimensional Regions

    • Jonathan Goldstein, John C. Plat, Christopher J. C. Burges
    Pages 137-158

  11. Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis

    • Stephen Roberts, Rizwan Choudrey
    Pages 159-179

  12. Ensemble Algorithms for Feature Selection

    • Jeremy D. Rogers, Steve R. Gunn
    Pages 180-198

  13. Can Gaussian Process Regression Be Made Robust Against Model Mismatch?

    • Peter Sollich
    Pages 199-210

  14. Understanding Gaussian Process Regression Using the Equivalent Kernel

    • Peter Sollich, Christopher K. I. Williams
    Pages 211-228

  15. Integrating Binding Site Predictions Using Non-linear Classification Methods

    • Yi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alistair Rust, Neil Davey
    Pages 229-241

  16. Support Vector Machine to Synthesise Kernels

    • Hongying Meng, John Shawe-Taylor, Sandor Szedmak, Jason D. R. Farquhar
    Pages 242-255

  17. Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data

    • Bram Vanschoenwinkel, Bernard Manderick
    Pages 256-280

  18. Variational Bayes Estimation of Mixing Coefficients

    • Bo Wang, D. M. Titterington
    Pages 281-295

  19. A Comparison of Condition Numbers for the Full Rank Least Squares Problem

    • Joab R. Winkler
    Pages 296-318

  20. SVM Based Learning System for Information Extraction

    • Yaoyong Li, Kalina Bontcheva, Hamish Cunningham
    Pages 319-339
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Other volumes

  1. Deterministic and Statistical Methods in Machine Learning

Editors and Affiliations

  • Department of Computer Science, The University of Sheffield, Sheffield, UK

    Joab Winkler, Mahesan Niranjan

  • University of Manchester, UK

    Neil Lawrence

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