Skip to main content
SpringerLink
Log in
Book cover

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Mahesan Niranjan

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Neil Lawrence

    1. University of Manchester, UK

    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 3635)

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

Included in the following conference series:

Conference proceedings info: DSMML 2004.

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

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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 (19 papers)

  1. Front Matter

    Download chapter PDF

  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
Back to top

Other volumes

  1. Deterministic and Statistical Methods in Machine Learning

Keywords

Editors and Affiliations

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

    Joab Winkler, Mahesan Niranjan

  • University of Manchester, UK

    Neil Lawrence

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation