Deterministic and statistical methods in machine learning : first international workshop, Sheffield, UK, September 7-10, 2004 : revised lectures / Joab Winkler, Mahesan Niranjan, Neil Lawrence (eds.)
Object Recognition via Local Patch Labelling -- Multi Channel Sequence Processing -- Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis -- Extensions of the Informative Vector Machine -- Efficient Communication by Breathing -- Guiding Local Regression Using Visualisation -- Transformations of Gaussian Process Priors -- Kernel Based Learning Methods: Regularization Networks and RBF Networks -- Redundant Bit Vectors for Quickly Searching High-Dimensional Regions -- Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis -- Ensemble Algorithms for Feature Selection -- Can Gaussian Process Regression Be Made Robust Against Model Mismatch? -- Understanding Gaussian Process Regression Using the Equivalent Kernel -- Integrating Binding Site Predictions Using Non-linear Classification Methods -- Support Vector Machine to Synthesise Kernels -- Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data -- Variational Bayes Estimation of Mixing Coefficients -- A Comparison of Condition Numbers for the Full Rank Least Squares Problem -- SVM Based Learning System for Information Extraction
Summary
Annotation This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. 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
