Conformal Prediction for Reliable Machine Learning

Theory, Adaptations and Applications

Edited by

  • Vineeth Balasubramanian, Department of Computer Science and Engineering, Indian Institute of Technology, Hyderabad, India
  • Shen-Shyang Ho, School of Computer Engineering, Nanyang Technological University, Singapore
  • Vladimir Vovk, Department of Computer Science, Royal Holloway University of London, UK

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.
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Audience

Primary Audience: Professors, Research Professors, Assistants/Associates, Professional Research/R&D Engineers, Research Scientists, Graduate Students

Secondary Audience: clinical researchers, researchers in biomedical informatics, national security, financial analysts, undergraduate students of applied machine learning, pattern recognition, artificial intelligence, clinical informatics

 

Book information

  • Published: April 2014
  • Imprint: MORGAN KAUFMANN
  • ISBN: 978-0-12-398537-8

Reviews

"...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




Table of Contents

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