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Neural networks for conditional probability estimation : forecasting beyond point predictions

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Cover of 'Neural networks for conditional probability estimation : forecasting beyond point predictions'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 A Universal Approximator Network for Predicting Conditional Probability Densities
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    Chapter 3 A Maximum Likelihood Training Scheme
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    Chapter 4 Benchmark Problems
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    Chapter 5 Demonstration of the Model Performance on the Benchmark Problems
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    Chapter 6 Random Vector Functional Link (RVFL) Networks
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    Chapter 7 Improved Training Scheme Combining the Expectation Maximisation (EM) Algorithm with the RVFL Approach
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    Chapter 8 Empirical Demonstration: Combining EM and RVFL
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    Chapter 9 A simple Bayesian regularisation scheme
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    Chapter 10 The Bayesian Evidence Scheme for Regularisation
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    Chapter 11 The Bayesian Evidence Scheme for Model Selection
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    Chapter 12 Demonstration of the Bayesian Evidence Scheme for Regularisation
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    Chapter 13 Network Committees and Weighting Schemes
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    Chapter 14 Demonstration: Committees of Networks Trained with Different Regularisation Schemes
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    Chapter 15 Automatic Relevance Determination (ARD)
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    Chapter 16 A Real-World Application: The Boston Housing Data
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    Chapter 17 Summary
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    Chapter 18 Appendix: Derivation of the Hessian for the Bayesian Evidence Scheme
Attention for Chapter 15: Automatic Relevance Determination (ARD)
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Citations

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Chapter title
Automatic Relevance Determination (ARD)
Chapter number 15
Book title
Neural Networks for Conditional Probability Estimation
Published by
Springer, London, January 1999
DOI 10.1007/978-1-4471-0847-4_15
Book ISBNs
978-1-85233-095-8, 978-1-4471-0847-4
Authors

Dirk Husmeier

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 14%
Researcher 1 7%
Lecturer 1 7%
Student > Master 1 7%
Unknown 9 64%
Readers by discipline Count As %
Mathematics 2 14%
Medicine and Dentistry 1 7%
Unknown 11 79%

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