Joab Winkler, Mahesan Niranjan, Neil D. Lawrence (Eds.):
Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures.
Lecture Notes in Computer Science 3635 Springer 2005, ISBN 3-540-29073-7 
- Christopher M. Bishop, Ilkay Ulusoy:
Object Recognition via Local Patch Labelling.
1-21
- Samy Bengio, Hervé Bourlard:
Multi Channel Sequence Processing.
22-36
- Gavin C. Cawley, Nicola L. C. Talbot, Gareth J. Janacek, Michael Peck:
Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis.
37-55
- Neil D. Lawrence, John C. Platt, Michael I. Jordan:
Extensions of the Informative Vector Machine.
56-87
- Tom Shorrock, David MacKay, Chris Ball:
Efficient Communication by Breathing.
88-97
- Dharmesh M. Maniyar, Ian T. Nabney:
Guiding Local Regression Using Visualisation.
98-109
- Roderick Murray-Smith, Barak A. Pearlmutter:
Transformations of Gaussian Process Priors.
110-123
- Petra Kudová, Roman Neruda:
Kernel Based Learning Methods: Regularization Networks and RBF Networks.
124-136
- Jonathan Goldstein, John C. Platt, Christopher J. C. Burges:
Redundant Bit Vectors for Quickly Searching High-Dimensional Regions.
137-158
- Stephen Roberts, Rizwan Choudrey:
Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis.
159-179
- Jeremy Rogers, Steve R. Gunn:
Ensemble Algorithms for Feature Selection.
180-198
- Peter Sollich:
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
199-210
- Peter Sollich, Christopher K. I. Williams:
Understanding Gaussian Process Regression Using the Equivalent Kernel.
211-228
- Yi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alistair G. Rust, Neil Davey:
Integrating Binding Site Predictions Using Non-linear Classification Methods.
229-241
- Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar:
Support Vector Machine to Synthesise Kernels.
242-255
- Bram Vanschoenwinkel, Bernard Manderick:
Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data.
256-280
- Bo Wang, D. M. Titterington:
Variational Bayes Estimation of Mixing Coefficients.
281-295
- Joab Winkler:
A Comparison of Condition Numbers for the Full Rank Least Squares Problem.
296-318
- Yaoyong Li, Kalina Bontcheva, Hamish Cunningham:
SVM Based Learning System for Information Extraction.
319-339
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