Artykuły udostępnione publicznie: - Mauro ScanagattaWięcej informacji
Dostępne w jakimś miejscu: 11
A survey on Bayesian network structure learning from data
M Scanagatta, A Salmerón, F Stella
Progress in Artificial Intelligence 8 (4), 425-439, 2019
Upoważnienia: Government of Spain
Learning Bayesian networks with thousands of variables
M Scanagatta, CP de Campos, G Corani, M Zaffalon
Advances in neural information processing systems 28, 2015
Upoważnienia: Swiss National Science Foundation
Air pollution prediction via multi-label classification
G Corani, M Scanagatta
Environmental modelling & software 80, 259-264, 2016
Upoważnienia: Swiss National Science Foundation
Learning treewidth-bounded Bayesian networks with thousands of variables
M Scanagatta, G Corani, CP De Campos, M Zaffalon
Advances in neural information processing systems 29, 2016
Upoważnienia: Swiss National Science Foundation
Entropy-based pruning for learning Bayesian networks using BIC
CP de Campos, M Scanagatta, G Corani, M Zaffalon
Artificial Intelligence 260, 42-50, 2018
Upoważnienia: Swiss National Science Foundation
Approximate structure learning for large Bayesian networks
M Scanagatta, G Corani, CP De Campos, M Zaffalon
Machine Learning 107, 1209-1227, 2018
Upoważnienia: Swiss National Science Foundation
Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets
M Scanagatta, G Corani, M Zaffalon, J Yoo, U Kang
International Journal of Approximate Reasoning 95, 152-166, 2018
Upoważnienia: Swiss National Science Foundation
Improved local search in Bayesian networks structure learning
M Scanagatta, G Corani, M Zaffalon
Advanced methodologies for Bayesian networks, 45-56, 2017
Upoważnienia: Swiss National Science Foundation
Learning extended tree augmented naive structures
CP de Campos, G Corani, M Scanagatta, M Cuccu, M Zaffalon
International Journal of Approximate Reasoning 68, 153-163, 2016
Upoważnienia: Swiss National Science Foundation
Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference
J Yoo, U Kang, M Scanagatta, G Corani, M Zaffalon
Proceedings of the 13th International Conference on Web Search and Data …, 2020
Upoważnienia: Swiss National Science Foundation
Min-BDeu and max-BDeu scores for learning Bayesian networks
M Scanagatta, CP de Campos, M Zaffalon
Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht …, 2014
Upoważnienia: Swiss National Science Foundation
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