Téléchargez le livre : Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning

Theory, Adaptations and Applications

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Éditeur :

Morgan Kaufmann


Paru le : 2014-04-23



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Description
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 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
Pages
334 pages
Collection
n.c
Parution
2014-04-23
Marque
Morgan Kaufmann
EAN papier
9780123985378
EAN PDF
9780124017153
Informations sur l'ebook
Nombre pages copiables
33
Nombre pages imprimables
33
Taille du fichier
14213 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
95,95 €
EAN EPUB SANS DRM
9780124017153
Informations sur l'ebook
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
95,95 €

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