Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

by Vladimir Vovk, Shen-Shyang Ho, Vineeth Balasubramanian

Book Overview

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...

• Format: Paperback
• Language: English
• ISBN: 0123985374
• ISBN13: 9780123985378
• Release Date: May 2014
• Publisher: Morgan Kaufmann Publishers
• Length: 334 Pages
• Weight: 1.47 lbs.
• Dimensions: 0.6" x 7.7" x 9.2"