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Deterministic and statistical methods in machine learning : first international workshop, Sheffield, UK, September 7-10, 2004 : revised lectures

Overview of attention for book
Cover of 'Deterministic and statistical methods in machine learning : first international workshop, Sheffield, UK, September 7-10, 2004 : revised lectures'

Table of Contents

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    Book Overview
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    Chapter 1 Object Recognition via Local Patch Labelling
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    Chapter 2 Multi Channel Sequence Processing
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    Chapter 3 Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis
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    Chapter 4 Extensions of the Informative Vector Machine
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    Chapter 5 Efficient Communication by Breathing
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    Chapter 6 Guiding Local Regression Using Visualisation
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    Chapter 7 Transformations of Gaussian Process Priors
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    Chapter 8 Kernel Based Learning Methods: Regularization Networks and RBF Networks
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    Chapter 9 Redundant Bit Vectors for Quickly Searching High-Dimensional Regions
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    Chapter 10 Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis
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    Chapter 11 Ensemble Algorithms for Feature Selection
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    Chapter 12 Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
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    Chapter 13 Understanding Gaussian Process Regression Using the Equivalent Kernel
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    Chapter 14 Integrating Binding Site Predictions Using Non-linear Classification Methods
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    Chapter 15 Support Vector Machine to Synthesise Kernels
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    Chapter 16 Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data
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    Chapter 17 Variational Bayes Estimation of Mixing Coefficients
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    Chapter 18 A Comparison of Condition Numbers for the Full Rank Least Squares Problem
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    Chapter 19 SVM Based Learning System for Information Extraction
Attention for Chapter 4: Extensions of the Informative Vector Machine
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Citations

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54 Mendeley
Summary Dimensions citations
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Chapter title
Extensions of the Informative Vector Machine
Chapter number 4
Book title
Deterministic and Statistical Methods in Machine Learning
Published in
Lecture notes in computer science, January 2005
DOI 10.1007/11559887_4
Book ISBNs
978-3-54-029073-5, 978-3-54-031728-9
Authors

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

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 2%
Germany 1 2%
United Kingdom 1 2%
Slovenia 1 2%
Belgium 1 2%
Spain 1 2%
United States 1 2%
Unknown 47 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 31%
Researcher 11 20%
Professor 6 11%
Professor > Associate Professor 6 11%
Student > Postgraduate 3 6%
Other 8 15%
Unknown 3 6%
Readers by discipline Count As %
Computer Science 29 54%
Engineering 10 19%
Mathematics 4 7%
Chemistry 2 4%
Business, Management and Accounting 1 2%
Other 5 9%
Unknown 3 6%

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