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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 |
| _version_ | 1780936281720619008 |
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| author | Balasubramanian, Vineeth Ho, Shen-Shyang Vovk, Vladimir |
| author_facet | Balasubramanian, Vineeth Ho, Shen-Shyang Vovk, Vladimir |
| author_sort | Balasubramanian, Vineeth |
| collection | CERN |
| description | 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 detecti |
| id | cern-1701700 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2014 |
| publisher | Morgan Kaufmann |
| record_format | invenio |
| spelling | cern-17017002021-04-21T21:02:08Zhttp://cds.cern.ch/record/1701700engBalasubramanian, VineethHo, Shen-ShyangVovk, VladimirConformal prediction for reliable machine learning: theory, adaptations and applicationsComputing and ComputersThe 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 detectiMorgan Kaufmannoai:cds.cern.ch:17017002014 |
| spellingShingle | Computing and Computers Balasubramanian, Vineeth Ho, Shen-Shyang Vovk, Vladimir Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title | Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title_full | Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title_fullStr | Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title_full_unstemmed | Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title_short | Conformal prediction for reliable machine learning: theory, adaptations and applications |
| title_sort | conformal prediction for reliable machine learning: theory, adaptations and applications |
| topic | Computing and Computers |
| url | http://cds.cern.ch/record/1701700 |
| work_keys_str_mv | AT balasubramanianvineeth conformalpredictionforreliablemachinelearningtheoryadaptationsandapplications AT hoshenshyang conformalpredictionforreliablemachinelearningtheoryadaptationsandapplications AT vovkvladimir conformalpredictionforreliablemachinelearningtheoryadaptationsandapplications |