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Methods and Procedures for the Verification and Validation of Artificial Neural Networks
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Background of the Verification and Validation of Neural Networks
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Chapter 2
Augmentation of Current Verification and Validation Practices
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Chapter 3
Risk and Hazard Analysis for Neural Network Systems
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Chapter 4
Validation of Neural Networks Via Taxonomic Evaluation
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Chapter 5
Stability Properties of Neural Networks
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Chapter 6
Neural Network Verification
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Chapter 7
Neural Network Visualization Techniques
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Chapter 8
Rule Extraction as a Formal Method
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Chapter 9
Automated Test Generation for Testing Neural Network Systems
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Chapter 10
Run-Time Assessment of Neural Network Control Systems
Overall attention for this book and its chapters
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Mentioned by
syllabi
1
institution with syllabi
Citations
dimensions_citation
49
Dimensions
Readers on
mendeley
138
Mendeley
Book overview
1. Background of the Verification and Validation of Neural Networks
2. Augmentation of Current Verification and Validation Practices
3. Risk and Hazard Analysis for Neural Network Systems
4. Validation of Neural Networks Via Taxonomic Evaluation
5. Stability Properties of Neural Networks
6. Neural Network Verification
7. Neural Network Visualization Techniques
8. Rule Extraction as a Formal Method
9. Automated Test Generation for Testing Neural Network Systems
10. Run-Time Assessment of Neural Network Control Systems
Summary
Syllabi
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
2
syllabi from
1
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
Unknown
2
Business, Engineering, Biology, History, Law