Conformal Prediction for Reliable Machine Learning

Theory, Adaptations and Applications

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Conformal Prediction for Reliable Machine Learning by , Elsevier Science
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9780124017153
Publisher: Elsevier Science Publication: April 23, 2014
Imprint: Morgan Kaufmann Language: English
Author:
ISBN: 9780124017153
Publisher: Elsevier Science
Publication: April 23, 2014
Imprint: Morgan Kaufmann
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Elsevier Science

Cover of the book Handbook of Industrial Organization by
Cover of the book Non-Crimp Fabric Composites by
Cover of the book Two-Component Signaling Systems, Part B by
Cover of the book Mechanics, Analysis and Geometry: 200 Years after Lagrange by
Cover of the book Food for the Ageing Population by
Cover of the book Advances in Immunology by
Cover of the book Target Validation in Drug Discovery by
Cover of the book Homology Effects by
Cover of the book High Voltage Engineering Fundamentals by
Cover of the book GERD by
Cover of the book Translating Gene Therapy to the Clinic by
Cover of the book Fundamental Principles of Engineering Nanometrology by
Cover of the book Biomaterials in Translational Medicine by
Cover of the book Allergy, Immunity and Tolerance in Early Childhood by
Cover of the book Pollution Control and Resource Recovery by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy