Zobrazeno 1 - 10
of 1 296
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:
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
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:
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
Recent evidence highlights the usefulness of DNA methylation (DNAm) biomarkers as surrogates for exposure to risk factors for non-communicable diseases in epidemiological studies and randomized trials. DNAm variability has been demonstrated to be tig
Externí odkaz:
http://arxiv.org/abs/2112.12719
Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these models is jeopardized in high-dimensional spaces, where they tend to
Externí odkaz:
http://arxiv.org/abs/2105.07935
Publikováno v:
Biogeosciences, Vol 20, Pp 3261-3271 (2023)
Headwater streams are important sources of greenhouse gases to the atmosphere. The magnitude of gas emissions originating from such streams, however, is modulated by the characteristic microtopography of the riverbed, which might promote the spatial
Externí odkaz:
https://doaj.org/article/ad7157db70454c4fb92e798d3749b711
Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for t
Externí odkaz:
http://arxiv.org/abs/2010.10415
The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection in model-based classification have been proposed. Surprisingly, t
Externí odkaz:
http://arxiv.org/abs/2007.14810
Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and co
Externí odkaz:
http://arxiv.org/abs/2006.09012