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Conformal Prediction for Reliable Machine Learning
Theory, Adaptations and Applications
An introduction to the theory of the conformal prediction framework, with practical applications in medicine, finance, and more
Vineeth Balasubramanian (Edited by), Shen-Shyang Ho (Edited by), Vladimir Vovk (Edited by)
9780123985378, Elsevier Science
Paperback, published 10 June 2014
334 pages
23.4 x 19 x 2.2 cm, 0.7 kg
"...captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection." --Zentralblatt MATH, Sep-14 "...the book is highly recommended for people looking for formal machine learning techniques that can guarantee theoretical soundness and reliability." --Computing Reviews,December 4,2014 "This book captures the basic theory of the framework, demonstrates how the framework can be applied to real-world problems, and also presents several adaptations of the framework…" --HPCMagazine.com, August 2014
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.
Section I: Theory 1. The Basic Conformal Prediction Framework 2. Beyond the Basic Conformal Prediction Framework Section II: Adaptations 3. Active Learning using Conformal Prediction 4. Anomaly Detection 5. Online Change Detection by Testing Exchangeability 6. Feature Selection and Conformal Predictors 7. Model Selection 8. Quality Assessment 9. Other Adaptations Section III: Applications 10. Biometrics 11. Diagnostics and Prognostics by Conformal Predictors 12. Biomedical Applications using Conformal Predictors 13. Reliable Network Traffic Classification and Demand Prediction 14. Other Applications
Subject Areas: Machine learning [UYQM], Artificial intelligence [UYQ]
