ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints - Mines Saint-Étienne
Conference Papers Year : 2020

ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

Domains

Artificial Intelligence [cs.AI]

Benjamin DALMAS  : Connect in order to contact the contributor

https://hal-emse.ccsd.cnrs.fr/emse-03128243

Submitted on : Tuesday, February 2, 2021-10:08:26 AM

Last modification on : Tuesday, September 17, 2024-3:45:47 PM

No file

Dates and versions

emse-03128243 , version 1 (02-02-2021)

Identifiers

Cite

Chen He, Benjamin Dalmas, Xiaolan Xie. ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Aug 2020, Hong Kong, France. pp.55-60, ⟨10.1109/CASE48305.2020.9216791⟩. ⟨emse-03128243⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

EMSE PRES_CLERMONT CNRS CIS-ENSMSE LIMOS I4S-ENSMSE CLERMONT-AUVERGNE-INP INSTITUT-MINES-TELECOM
83 View
0 Download

Altmetric

Share

More