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By:Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Published on 2014-04-23 by Newnes


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

This Book was ranked at 27 by Google Books for keyword Machine Learning.

Book ID of Conformal Prediction for Reliable Machine Learning's Books is pgfUAgAAQBAJ, Book which was written byVineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovkhave ETAG "YW0cWcpSZiw"

Book which was published by Newnes since 2014-04-23 have ISBNs, ISBN 13 Code is 9780124017153 and ISBN 10 Code is 0124017150

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Conformal Prediction for Reliable Machine Learning -Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk- 9780124017153

Conformal Prediction for Reliable Machine Learning -Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk- 0124017150

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