Conformal prediction for reliable machine learning theory, adaptations and applications
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 ri...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
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Waltham, MA
Morgan Kaufmann
2014
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| Edition: | 1st ed |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | 1 volume illustrations |
| ISBN: | 0123985374 0124017150 9780124017153 |