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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.

Contributor(s): Winkler, Joab [editor.] | Lawrence, Neil [editor.] | Niranjan, Mahesan [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 3635Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005Edition: 1st ed. 2005.Description: VIII, 341 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540317289.Subject(s): 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 RecognitionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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.
In: Springer Nature eBook
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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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