Conformal Prediction for Reliable Machine Learning Theory, Adaptations and Applications
Coordonnateurs : Balasubramanian Vineeth, Ho Shen-Shyang, Vovk Vladimir

- Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning
- Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering
- Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
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
Date de parution : 06-2014
Ouvrage de 298 p.
19.1x23.5 cm