Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

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

Ho, Shen-Shyang
Vovk, Vladimir
Balasubramanian, Vineeth

Paperback

General Computers

ISBN10: 0123985374
ISBN13: 9780123985378
Publisher: Morgan Kaufmann Publishers
Published: Apr 29 2014
Pages: 334
Weight: 1.47
Height: 0.60 Width: 7.65 Depth: 9.23
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.

Also in

General Computers

View All