On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions
| Autor: | Frédéric Pichon, Siti Mutmainah, Samir Hachour, David Mercier |
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| Přispěvatelé: | Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Université d'Artois (UA), UIN Sunan Kalijaga, Yogyakarta, Indonesia, Mercier, David |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Class (set theory) Ground truth business.industry Computer science Pattern recognition Proposition 02 engineering and technology Function (mathematics) Object (computer science) Measure (mathematics) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Contextual corrections Soft labels 020204 information systems Belief functions 0202 electrical engineering electronic engineering information engineering Learning set Learning 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 13th International Conference on Scalable Uncertainty Management, SUM 2019 13th International Conference on Scalable Uncertainty Management, SUM 2019, Dec 2019, Compiègne, France Lecture Notes in Computer Science ISBN: 9783030355135 SUM |
| Popis: | International audience; In this paper, a proposition is made to learn the parameters of evidential contextual correction mechanisms from a learning set composed of soft labelled data, that is data where the true class of each object is only partially known. The method consists in optimizing a measure of discrepancy between the values of the corrected contour function and the ground truth also represented by a contour function. The advantages of this method are illustrated by tests on synthetic and real data. |
| Databáze: | OpenAIRE |
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