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

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

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Winkler, Joab (Editor), Niranjan, Mahesan (Editor), Lawrence, Neil (Editor)
Format: Electronic eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
Series:Lecture Notes in Computer Science, 3635
Subjects:
Computer science.
Mathematical logic.
Database management.
Information storage and retrieval.
Artificial intelligence.
Image processing.
Pattern recognition.
Computer Science.
Artificial Intelligence (incl. Robotics).
Mathematical Logic and Formal Languages.
Database Management.
Information Storage and Retrieval.
Image Processing and Computer Vision.
Pattern Recognition.
Online Access:Full Text via HEAL-Link
Table of Contents:
  • 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.

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