Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications
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AlbakiReads Editorial A technical guide to confidence-aware machine-learning predictions
A technical guide to confidence-aware machine-learning predictions
Centered on conformal prediction, this edited volume examines a machine-learning framework designed to attach a measure of confidence to individual predictions. It combines foundational theory with applied uses in pattern-recognition contexts, including medical diagnosis, face recognition, and financial risk prediction. The collection also covers adaptations of the framework for active learning, change detection, and anomaly detection, connecting uncertainty estimation with practical modeling tasks. Framed as both a reference for implementation and a basis for continued research, the book maps how conformal methods can be extended across real-world prediction problems.
Good fit for readers who enjoy
- Machine-learning researchers studying uncertainty and prediction confidence
- Practitioners working with pattern recognition or risk-sensitive prediction tasks
- Readers seeking material on active learning, change detection, or anomaly detection
Themes
Conformal predictionUncertainty quantificationPattern recognitionActive learningAnomaly detection
Based on publisher information and book metadata.
Book Overview The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with ...
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.
Book Details Format: Paperback | Pages: 334 | Language: English | Publisher: MORGAN KAUFMANN PUBL INC | ISBN: 0123985374
FormatPaperback
Pages334
LanguageEnglish
ISBN0123985374
EAN9780123985378
PublisherMORGAN KAUFMANN PUBL INC
Publication Date2014-04-29
Edition1
AccessoriesNo Accessory
ConditionNew
Product TypeQUALITY PAPERBACK BOOKS
Weight1.47 Pounds
Length9.23 Inches
Width7.65 Inches
Height0.6 Inches
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