Deterministic and Statistical Methods in Machine Learning (eBook, PDF)

First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
Redaktion: Winkler, Joab; Niranjan, Mahesan; Lawrence, Neil

• Format: PDF

Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 341
• Erscheinungstermin: 17. Oktober 2005
• Englisch
• ISBN-13: 9783540317289
• Artikelnr.: 44227986

Autorenporträt

Joab Winkler, University of Sheffield, UK / Neil Lawrence, University of Sheffield, UK / Mahesan Niranjan, University of Sheffield, UK

Inhaltsangabe

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.