CAPPOZZO, ANDREA
CAPPOZZO, ANDREA
DIPARTIMENTO DI STATISTICA E METODI QUANTITATIVI
Wine authenticity assessed via trimming
2017 Greselin, F; Cappozzo, A
Robust Updating Classification Rule with applications in Food Authenticity Studies
2018 Cappozzo, A; Greselin, F; Murphy, T
The role of trimming and variable selection in robust model-based classification for food authenticity studies
2018 Cappozzo, A; Greselin, F; Murphy, B
An object oriented approach to multimodal imaging data in neuroscience
2018 Cappozzo, A; Ferraccioli, F; Stefanucci, M; Secchi, P
Detecting wine adulterations employing robust mixture of factor analyzers
2019 Cappozzo, A; Greselin, F
Supervised learning in presence of outliers, label noise and unobserved classes
2019 Cappozzo, A; Greselin, F; Murphy, T
Predicting and improving smart mobility: a robust model-based approach to the BikeMi BSS
2019 Cappozzo, A; Greselin, F; Manzi, G
A robust approach to model-based classification based on trimming and constraints: Semi-supervised learning in presence of outliers and label noise
2020 Cappozzo, A; Greselin, F; Murphy, T
Bayesian nonparametric adaptive classification with robust prior information
2020 Denti, F; Cappozzo, A; Greselin, F
Variable selection for robust model-based learning from contaminated data
2020 Cappozzo, A; Greselin, F; Murphy, B
Anomaly and Novelty detection for robust semi-supervised learning
2020 Cappozzo, A; Greselin, F; Murphy, T
Robust model-based classification and clustering: advances in learning from contaminated datasets
2020 Cappozzo, A
Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images
2021 Cappozzo, A; Greselin, F; Murphy, T
Monitoring tools for Cluster Weighted Robust Models
2021 Cappozzo, A; Greselin, F
Robust variable selection for model-based learning in presence of adulteration
2021 Cappozzo, A; Greselin, F; Murphy, T
Exploring solutions via monitoring for cluster weighted robust models
2021 Cappozzo, A; Garcìa-Escudero, L; Greselin, F; Mayo-Iscar, A
Robust classification of spectroscopic data in agri-food: first analysis on the stability of results
2021 Cappozzo, A; Duponchel, L; Greselin, F; Murphy Thomas, B
Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food
2021 Cappozzo, A; Duponchel, L; Greselin, F; Murphy, T
Correction to: A two-stage Bayesian semiparametricmodel for novelty detection with robust prior information (Statistics and Computing, (2021), 31, 4, (42), 10.1007/s11222-021-10017-7)
2022 Denti, F; Cappozzo, A; Greselin, F
Outlier and Novelty Detection for Functional Data: a Semiparametric BayesianApproach
2022 Denti, F; Cappozzo, A; Greselin, F