Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures - researchr publication

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Joab Winkler, Mahesan Niranjan, Neil D. Lawrence, editors, Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures. Volume 3635 of Lecture Notes in Computer Science, Springer, 2005.

Conference: dsmml2005

Abstract is missing.

Object Recognition via Local Patch LabellingChristopher M. Bishop, Ilkay Ulusoy. 1-21 [doi]

Multi Channel Sequence ProcessingSamy Bengio, Hervé Bourlard. 22-36 [doi]

Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival AnalysisGavin C. Cawley, Nicola L. C. Talbot, Gareth J. Janacek, Michael Peck. 37-55 [doi]

Extensions of the Informative Vector MachineNeil D. Lawrence, John C. Platt, Michael I. Jordan. 56-87 [doi]

Efficient Communication by BreathingTom Shorrock, David Mackay, Chris Ball. 88-97 [doi]

Guiding Local Regression Using VisualisationDharmesh M. Maniyar, Ian T. Nabney. 98-109 [doi]

Transformations of Gaussian Process PriorsRoderick Murray-Smith, Barak A. Pearlmutter. 110-123 [doi]

Kernel Based Learning Methods: Regularization Networks and RBF NetworksPetra Kudová, Roman Neruda. 124-136 [doi]

Redundant Bit Vectors for Quickly Searching High-Dimensional RegionsJonathan Goldstein, John C. Platt, Christopher J. C. Burges. 137-158 [doi]

Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal AnalysisStephen Roberts, Rizwan Choudrey. 159-179 [doi]

Ensemble Algorithms for Feature SelectionJeremy Rogers, Steve R. Gunn. 180-198 [doi]

Can Gaussian Process Regression Be Made Robust Against Model Mismatch?Peter Sollich. 199-210 [doi]

Understanding Gaussian Process Regression Using the Equivalent KernelPeter Sollich, Christopher K. I. Williams. 211-228 [doi]

Integrating Binding Site Predictions Using Non-linear Classification MethodsYi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alistair G. Rust, Neil Davey. 229-241 [doi]

Support Vector Machine to Synthesise KernelsHongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar. 242-255 [doi]

Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic DataBram Vanschoenwinkel, Bernard Manderick. 256-280 [doi]

Variational Bayes Estimation of Mixing CoefficientsBo Wang, D. M. Titterington. 281-295 [doi]

A Comparison of Condition Numbers for the Full Rank Least Squares ProblemJoab Winkler. 296-318 [doi]

SVM Based Learning System for Information ExtractionYaoyong Li, Kalina Bontcheva, Hamish Cunningham. 319-339 [doi]

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