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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.
Understand the theoretical foundations of this important framework
that can provide a reliable measure of confidence with predictions
in machine learningBe able to apply this framework to real-world
problems in different machine learning settings, including
classification, regression, and clusteringLearn effective ways of
adapting the framework to newer problem settings, such as active
learning, model selection, or change detection
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