book

Conformal Prediction for Reliable Machine Learning

by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

April 2014

Intermediate to advanced

334 pages

10h 40m

English

Morgan Kaufmann

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Cover image
Title page
Copyright
Copyright Permissions
Chapter 3Chapter 6Chapter 9Chapter 10Chapter 11
Contributing Authors
Foreword
Preface
Book OrganizationPart I: TheoryPart II: AdaptationsPart III: ApplicationsCompanion WebsiteContacting UsAcknowledgments
Part 1: Theory
Chapter 1. The Basic Conformal Prediction Framework
AbstractAcknowledgments1.1 The Basic Setting and Assumptions1.2 Set and Confidence Predictors1.3 Conformal Prediction1.4 Efficiency in the Case of Prediction without Objects1.5 Universality of Conformal Predictors1.6 Structured Case and Classification1.7 Regression1.8 Additional Properties of Validity and Efficiency in the Online Framework
Chapter 2. Beyond the Basic Conformal Prediction Framework
AbstractAcknowledgments2.1 Conditional Validity2.2 Conditional Conformal Predictors2.3 Inductive Conformal Predictors2.4 Training Conditional Validity of Inductive Conformal Predictors2.5 Classical Tolerance Regions

2.6 Object Conditional Validity and Efficiency
2.7 Label Conditional Validity and ROC Curves2.8 Venn Predictors
Part 2: Adaptations
Chapter 3. Active Learning
AbstractAcknowledgments3.1 Introduction3.2 Background and Related Work3.3 Active Learning Using Conformal Prediction3.4 Experimental Results3.5 Discussion and Conclusions
Chapter 4. Anomaly Detection
Abstract4.1 Introduction4.2 Background4.3 Conformal Prediction for Multiclass Anomaly Detection4.4 Conformal Anomaly Detection4.5 Inductive Conformal Anomaly Detection4.6 Nonconformity Measures for Examples Represented as Sets of Points4.7 Sequential Anomaly Detection in Trajectories4.8 Conclusions
Chapter 5. Online Change Detection
Abstract5.1 Introduction5.2 Related Work5.3 Background5.4 A Martingale Approach for Change Detection5.5 Experimental Results5.6 Implementation Issues5.7 Conclusions
Chapter 6. Feature Selection
Abstract6.1 Introduction6.2 Feature Selection Methods6.3 Issues in Feature Selection6.4 Feature Selection for Conformal Predictors6.5 Discussion and Conclusions
Chapter 7. Model Selection
AbstractAcknowledgments7.1 Introduction7.2 Background7.3 SVM Model Selection Using Nonconformity Measure7.4 Nonconformity Generalization Error Bound7.5 Experimental Results7.6 Conclusions
Chapter 8. Prediction Quality Assessment
AbstractAcknowledgments8.1 Introduction8.2 Related Work8.3 Generalized Transductive Reliability Estimation8.4 Experimental Results8.5 Discussion and Conclusions
Chapter 9. Other Adaptations
AbstractAcknowledgments9.1 Introduction9.2 Metaconformal Predictors9.3 Single-Stacking Conformal Predictors9.4 Conformal Predictors for Time Series Analysis9.5 Conclusions
Part 3: Applications
Chapter 10. Biometrics and Robust Face Recognition
Abstract10.1 Introduction10.2 Biometrics and Forensics10.3 Face Recognition10.4 Randomness and Complexity10.5 Transduction10.6 Nonconformity Measures for Face Recognition10.7 Open and Closed Set Face Recognition10.8 Watch List and Surveillance10.9 Score Normalization10.10 Recognition-by-Parts Using Transduction and Boosting10.11 Reidentification Using Sensitivity Analysis and Revision10.12 Conclusions
Chapter 11. Biomedical Applications: Diagnostic and Prognostic
AbstractAcknowledgments11.1 Introduction11.2 Examples of Medical Diagnostics11.3 Nonconformity Measures for Medical and Biological Applications11.4 Discussion and Conclusions
Chapter 12. Network Traffic Classification and Demand Prediction
Abstract12.1 Introduction12.2 Network Traffic Classification12.3 Network Demand Prediction12.4 Experimental Results12.5 Conclusions
Chapter 13. Other Applications
Abstract13.1 Nuclear Fusion Device Applications13.2 Sensor Device Applications13.3 Sustainability, Environment, and Civil Engineering13.4 Security Applications13.5 Applications from Other Domains
Bibliography
Index

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Conformal Prediction for Reliable Machine Learning

by Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk

Bibliography

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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