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Neural networks for conditional probability estimation : forecasting beyond point predictions
Overview of attention for book
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
Overall attention for this book and its chapters
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Mentioned by
syllabi
1
institution with syllabi
wikipedia
1
Wikipedia page
Citations
dimensions_citation
84
Dimensions
Readers on
mendeley
10
Mendeley
Book overview
1. Introduction
2. A Universal Approximator Network for Predicting Conditional Probability Densities
3. A Maximum Likelihood Training Scheme
4. Benchmark Problems
5. Demonstration of the Model Performance on the Benchmark Problems
6. Random Vector Functional Link (RVFL) Networks
7. Improved Training Scheme Combining the Expectation Maximisation (EM) Algorithm with the RVFL Approach
8. Empirical Demonstration: Combining EM and RVFL
9. A simple Bayesian regularisation scheme
10. The Bayesian Evidence Scheme for Regularisation
11. The Bayesian Evidence Scheme for Model Selection
12. Demonstration of the Bayesian Evidence Scheme for Regularisation
13. Network Committees and Weighting Schemes
14. Demonstration: Committees of Networks Trained with Different Regularisation Schemes
15. Automatic Relevance Determination (ARD)
16. A Real-World Application: The Boston Housing Data
17. Summary
18. Appendix: Derivation of the Hessian for the Bayesian Evidence Scheme
Summary
Syllabi
Wikipedia
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
1
syllabus from an institution on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
Georg-August Universität Göttingen
1
Performing Arts