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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...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
Morgan Kaufmann
2014
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/1701700 |