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Home > Deterministic and Statistical Methods in Machine Learning: First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures > Extensions of the Informative Vector Machine
Deterministic and Statistical Methods in Machine Learning: First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures |
Joab Winkler; Neil Lawrence; Mahesan Niranjan
Springer Berlin Heidelberg
Deterministic and Statistical Methods in Machine Learning: First International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
Extensions of the Informative Vector Machine
Authors

Neil D. Lawrence; John C. Platt; Michael I. Jordan

ARI Id

1664718307634_2900580

Access

Not Available Free

Pages

56-87

DOI

10.1007/11559887_4

Chapter URL

https://rd.springer.com/chapter/10.1007/11559887_4

Table of Contents of Book
Showing 1 to 20 of 21 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
Object Recognition via Local Patch Labelling
Christopher M. Bishop; Ilkay Ulusoy
1-21
Multi Channel Sequence Processing
Samy Bengio; Hervé Bourlard
22-36
Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis
Gavin C. Cawley; Nicola L. C. Talbot; Gareth J. Janacek; Michael W. Peck
37-55
Extensions of the Informative Vector Machine
Neil D. Lawrence; John C. Platt; Michael I. Jordan
56-87
Efficient Communication by Breathing
Tom H. Shorrock; David J. C. MacKay; Chris J. Ball
88-97
Guiding Local Regression Using Visualisation
Dharmesh M. Maniyar; Ian T. Nabney
98-109
Transformations of Gaussian Process Priors
Roderick Murray-Smith; Barak A. Pearlmutter
110-123
Kernel Based Learning Methods: Regularization Networks and RBF Networks
Petra Kudová; Roman Neruda
124-136
Redundant Bit Vectors for Quickly Searching High-Dimensional Regions
Jonathan Goldstein; John C. Plat; Christopher J. C. Burges
137-158
Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis
Stephen Roberts; Rizwan Choudrey
159-179
Ensemble Algorithms for Feature Selection
Jeremy D. Rogers; Steve R. Gunn
180-198
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
Peter Sollich
199-210
Understanding Gaussian Process Regression Using the Equivalent Kernel
Peter Sollich; Christopher K. I. Williams
211-228
Integrating Binding Site Predictions Using Non-linear Classification Methods
Yi Sun; Mark Robinson; Rod Adams; Paul Kaye; Alistair Rust; Neil Davey
229-241
Support Vector Machine to Synthesise Kernels
Hongying Meng; John Shawe-Taylor; Sandor Szedmak; Jason D. R. Farquhar
242-255
Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data
Bram Vanschoenwinkel; Bernard Manderick
256-280
Variational Bayes Estimation of Mixing Coefficients
Bo Wang; D. M. Titterington
281-295
A Comparison of Condition Numbers for the Full Rank Least Squares Problem
Joab R. Winkler
296-318
SVM Based Learning System for Information Extraction
Yaoyong Li; Kalina Bontcheva; Hamish Cunningham
319-339
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 21 entries
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