DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks
- DOI
- 10.1016/j.ijar.2025.109400
- Published
- 2025-06
- Container
- International Journal of Approximate Reasoning
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ijar.2025.109400,
title = {DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks},
author = {Loïc Guiziou and Emmanuel Ramasso and Sébastien Thibaud and Sébastien Denneulin},
year = {2025},
journal = {International Journal of Approximate Reasoning},
doi = {10.1016/j.ijar.2025.109400},
url = {https://doi.org/10.1016/j.ijar.2025.109400}
}RIS
TY - JOUR TI - DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks AU - Loïc Guiziou AU - Emmanuel Ramasso AU - Sébastien Thibaud AU - Sébastien Denneulin PY - 2025 JO - International Journal of Approximate Reasoning DO - 10.1016/j.ijar.2025.109400 UR - https://doi.org/10.1016/j.ijar.2025.109400 ER -
APA
Guiziou, L., Ramasso, E., Thibaud, S., & Denneulin, S. (2025). DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks. International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2025.109400
Source records
- crossref · retrieved 2026-09-25T10:29:45.839Z