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Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

by Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
No customer reviews yet ISBN 9780123985378 Morgan Kaufmann

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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BrandMorgan Kaufmann
Pub dateApr 29, 2014
ISBN-100123985374
ISBN-139780123985378
LanguageEnglish
Dimensions9.25 × 0.73 × 7.5 in
Weight2 lb
Last updated 2026-05-05 14:43
$138.90
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