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Deterministic and Statistical Methods in Machine Learning [electronic resource] : First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures / edited by Joab Winkler, Neil Lawrence, Mahesan Niranjan.

Medverkande: Materialtyp: TextSerie: Utgivningsuppgift: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005Utgåva: 1st ed. 2005Beskrivning: VIII, 341 p. online resourceInnehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9783540317289
Ämnen: DDK-klassifikation:
  • 006.3 23
Onlineresurser:
Innehåll:
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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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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