Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications-Reference-Cited by-同舟云学术

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Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications

Published:2021-09-06 Issue:4 Volume:24 Page:1731-1744
ISSN:1433-7541
Container-title:Pattern Analysis and Applications
language:en
Short-container-title:Pattern Anal Applic

Author:

Manouchehri Narges^ORCID,Bouguila Nizar,Fan Wentao

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds de Recherche du Québec - Nature et Technologies

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s10044-021-01023-6.pdf

Reference82 articles.

1. Amirkhani M, Manouchehri N, Bouguila N (2020) Fully Bayesian learning of multivariate beta mixture models. In: 2020 IEEE 21st international conference on Information Reuse and Integration for Data Science (IRI). IEEE, pp 120–127

2. Anuradha S, Satyanarayana C (2017) Medical image segmentation based on beta mixture distribution for effective identification of lesions. In: Recent developments in intelligent computing, communication and devices. Springer, pp 133–140

3. Bellot A, Schaar MVD (2020) Flexible modelling of longitudinal medical data: a Bayesian nonparametric approach. ACM Trans Comput Healthc 1(1):1–15

4. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

5. Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: a review for statisticians. J Am Stat Assoc 112(518):859–877

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3. 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

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