Deterministic and statistical methods in machine learning : first international workshop, Sheffield, UK, September 7-10, 2004 : revised lectures / / Joab Winkler, Mahesan Niranjan, Neil Lawrence (eds.)

• Record Nr.: UNINA9910483748103321
• Pubbl/distr/stampa: Berlin ; ; New York, : Springer, c2005
• ISBN: 3-540-31728-7
• Edizione: [1st ed. 2005.]
• Descrizione fisica: 1 online resource (VIII, 341 p.)
• Collana: Lecture notes in computer science. Lecture notes in artificial intelligence, , 0302-9743 ; ; 3635
• Altri autori (Persone): WinklerJoab; NiranjanMahesan; LawrenceNeil (Neil D.)
• Disciplina: 006.3
• Soggetti: Machine learning; Machine learning - Statistical methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Sheffield Machine Learning Workshop"--Pref.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.