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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Winkler, Joab (Επιμελητής έκδοσης), Niranjan, Mahesan (Επιμελητής έκδοσης), Lawrence, Neil (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Σειρά: Lecture Notes in Computer Science, 3635
• Θέματα: 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.

Πίνακας περιεχομένων:

• 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.