TY  - BOOK
AU  - Winkler,Joab
AU  - Niranjan,Mahesan
AU  - Lawrence,Neil
ED  - SpringerLink (Online service)
TI  - Deterministic and Statistical Methods in Machine Learning: First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures 
T2  - Lecture Notes in Computer Science,
SN  - 9783540317289
AV  - Q334-342 
U1  - 006.3 23
PY  - 2005///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Database management
KW  - Information storage and retrieval systems
KW  - Artificial intelligence
KW  - Computer vision
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Artificial Intelligence (incl. Robotics)
KW  - Mathematical Logic and Formal Languages
KW  - Database Management
KW  - Information Storage and Retrieval
KW  - Image Processing and Computer Vision
KW  - Pattern Recognition
N1  - 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
UR  - http://dx.doi.org/10.1007/11559887
ER  -