Expectation propagation learning of finite multivariate Beta mixture models and applications-Reference-Cited by-同舟云学术

Expectation propagation learning of finite multivariate Beta mixture models and applications

Published:2022-01-08 Issue:17 Volume:34 Page:14275-14285
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Manouchehri Narges^ORCID,Bouguila Nizar,Fan Wentao

Funder

Fonds de Recherche du Québec - Nature et Technologies

National Natural Science Foundation of China

natural sciences and engineering research council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06839-1.pdf

Reference48 articles.

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4. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Laak JAVD, Ginneken BV, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

5. Bertsimas D, Orfanoudaki A, Wiberg H (2021) Interpretable clustering: an optimization approach. Mach Learn 110(1):89–138

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2. Finite Multivariate McDonald's Beta Mixture Model Learning Approach in Medical Applications;Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing;2023-03-27

3. Expectation propagation learning of finite multivariate Beta mixture models and applications;Neural Computing and Applications;2022-01-08