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ORCIDDeterministic and Statistical Methods in Machine Learning 2004: Sheffield, UK
- Joab R. Winkler, Mahesan Niranjan, Neil D. Lawrence
:
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 W. 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 J. C. 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 J. 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 R. 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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