Skip to main content

Conformal Prediction for Reliable Machine Learning

Theory, Adaptations and Applications

  • 1st Edition - April 23, 2014
  • Editors: Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 1 2 - 3 9 8 5 3 7 - 8
  • eBook ISBN:
    9 7 8 - 0 - 1 2 - 4 0 1 7 1 5 - 3

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… Read more

Conformal Prediction for Reliable Machine Learning

Purchase options

Limited Offer

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Book bundle cover eBook and print

Institutional subscription on ScienceDirect

Request a sales quote
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.