Learning extended tree augmented naive structures
de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.
Accepted in International Journal of Approximate Reasoning. 68, pp. 153–163.
Air pollution prediction via multi-label classification
Corani, G. and Scanagatta, M.
Published in Environmental Modelling & Software 80, pp. 259–264.
Early classification of time series by hidden Markov models with set-valued parameters
Antonucci, A. and Scanagatta, M. and Mauà, D.D. and de Campos, C.P.
Published in Proceedings of the NIPS Time Series Workshop 2015.
Learning Bayesian networks with thousands of variables
Scanagatta, M. and de Campos, C.P. and Corani, G. and Zaffalon, M.
Published in NIPS 2015: Advances in Neural Information Processing Systems 28 NIPS.
Min-BDeu and max-BDeu scores for learning Bayesian networks
Scanagatta, M. and de Campos, C.P. and Zaffalon, M.
PPublished in PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models, pp. 426–441.
We developed a web-service for any Bayesian network structure learning task: BLIP.
We can take care of the whole process: learning the scores, learning the network, learning the parameters. You can simply provide your dataset and obtain your learned network!
Contact me: mauro [at] idsia [dot] ch