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

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Winkler, Joab (Editor ), Lawrence, Neil (Editor ), Niranjan, Mahesan (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edición:1st ed. 2005.
Colección:Lecture Notes in Artificial Intelligence, 3635
Temas:
Artificial intelligence.
Machine theory.
Database management.
Information storage and retrieval systems.
Computer vision.
Pattern recognition systems.
Artificial Intelligence.
Formal Languages and Automata Theory.
Database Management.
Information Storage and Retrieval.
Computer Vision.
Automated Pattern Recognition.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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