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