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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 7: Improved Training Scheme Combining the Expectation Maximisation (EM) Algorithm with the RVFL Approach
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Chapter title
Improved Training Scheme Combining the Expectation Maximisation (EM) Algorithm with the RVFL Approach
Chapter number 7
Book title
Neural Networks for Conditional Probability Estimation
Published by
Springer, London, January 1999
DOI 10.1007/978-1-4471-0847-4_7
Book ISBNs
978-1-85233-095-8, 978-1-4471-0847-4
Authors

Dirk Husmeier

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