Zobrazeno 1 - 10
of 6 908
pro vyhledávání: '"Cappozzo, A."'
Autor:
Cappozzo, Andrea, Casa, Alessandro
Covariance matrices provide a valuable source of information about complex interactions and dependencies within the data. However, from a clustering perspective, this information has often been underutilized and overlooked. Indeed, commonly adopted d
Externí odkaz:
http://arxiv.org/abs/2408.17040
Autor:
Filosa, Elsa
Publikováno v:
Annali d'Italianistica, 2020 Jan 01. 38, 466-468.
Externí odkaz:
https://www.jstor.org/stable/27119754
Autor:
Machera, Virginia
Publikováno v:
Annali d'Italianistica, 2020 Jan 01. 38, 464-466.
Externí odkaz:
https://www.jstor.org/stable/27119753
Autor:
Maselli, Matteo
Publikováno v:
Annali d'Italianistica, 2020 Jan 01. 38, 506-508.
Externí odkaz:
https://www.jstor.org/stable/27119768
Autor:
Caldera, Luca, Masci, Chiara, Cappozzo, Andrea, Forlani, Marco, Antonelli, Barbara, Leoni, Olivia, Ieva, Francesca
Evaluating hospitals' performance and its relation to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. Such a matter is particularly relevant in areas and situations where the healthcare system mus
Externí odkaz:
http://arxiv.org/abs/2405.11239
Autor:
Lodone, Michele
Publikováno v:
Aevum, 2019 May 01. 93(2), 596-599.
Externí odkaz:
https://www.jstor.org/stable/26906721
Autor:
Castaldo, Achille
Publikováno v:
Annali d'Italianistica, 2019 Jan 01. 37, 633-636.
Externí odkaz:
https://www.jstor.org/stable/26923355
Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are becoming increasingly prevalent in various fields. Despite its widespread adoption and successful application, this a
Externí odkaz:
http://arxiv.org/abs/2307.10673
Autor:
Gendre, Renato
Publikováno v:
Études romanes de Brno, Iss 2, Pp 231-232 (2019)
Externí odkaz:
https://doaj.org/article/efc91779531448298e028ff3d66e4ddf
Autor:
Benedetti, Luca, Boniardi, Eric, Chiani, Leonardo, Ghirri, Jacopo, Mastropietro, Marta, Cappozzo, Andrea, Denti, Francesco
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instances of an unlabeled test set while allowing for the presence of previo
Externí odkaz:
http://arxiv.org/abs/2212.01865