Christopher M. Bishop, Ilkay Ulusoy (auth.), Joab Winkler, Mahesan Niranjan, Neil Lawrence (eds.)3540290737, 9783540290735
The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Table of contents :
Front Matter….Pages –
Object Recognition via Local Patch Labelling….Pages 1-21
Multi Channel Sequence Processing….Pages 22-36
Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis….Pages 37-55
Extensions of the Informative Vector Machine….Pages 56-87
Efficient Communication by Breathing….Pages 88-97
Guiding Local Regression Using Visualisation….Pages 98-109
Transformations of Gaussian Process Priors….Pages 110-123
Kernel Based Learning Methods: Regularization Networks and RBF Networks….Pages 124-136
Redundant Bit Vectors for Quickly Searching High-Dimensional Regions….Pages 137-158
Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis….Pages 159-179
Ensemble Algorithms for Feature Selection….Pages 180-198
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?….Pages 199-210
Understanding Gaussian Process Regression Using the Equivalent Kernel….Pages 211-228
Integrating Binding Site Predictions Using Non-linear Classification Methods….Pages 229-241
Support Vector Machine to Synthesise Kernels….Pages 242-255
Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data….Pages 256-280
Variational Bayes Estimation of Mixing Coefficients….Pages 281-295
A Comparison of Condition Numbers for the Full Rank Least Squares Problem….Pages 296-318
SVM Based Learning System for Information Extraction….Pages 319-339
Back Matter….Pages –
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