Conformal Prediction for Reliable Machine Learning book cover

Conformal Prediction for Reliable Machine Learning

Theory, Adaptations and Applications

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.


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

Paperback, 334 Pages

Published: April 2014

Imprint: Morgan Kaufmann

ISBN: 978-0-12-398537-8


  • 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


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