Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Roberta Pappadà"'
Investigating thermal energy demand is crucial for the development of sustainable cities and efficient use of renewable sources. Despite the advances made in this field, the analysis of energy data provided by smart grids is currently a demanding cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a498bdcbd94556222453fdeae974505
https://doi.org/10.21203/rs.3.rs-1145716/v1
https://doi.org/10.21203/rs.3.rs-1145716/v1
The identification of groups’ prototypes, i.e. elements of a dataset that are representative of the group they belong to, is relevant to the tasks of clustering, classification and mixture modeling. The R package pivmet includes different methods f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::63d28eb057961e155fdd080e10f3cb42
http://hdl.handle.net/11368/2994386
http://hdl.handle.net/11368/2994386
In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated by the empirical issue of detecting low correlations and discriminating variables with very similar rank correlation. This issue arises from the analy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::54ae4d8526711de87cc070464d2c868a
http://hdl.handle.net/11368/2994383
http://hdl.handle.net/11368/2994383
Autor:
Elvira, Pelle, Roberta, Pappadà
The analysis of ego-network characteristics (especially size and composition) has become crucial in studying many aspects of everyday life. In this work, we propose a clustering procedure to find a partition of ego-networks into homogeneous groups ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dris___01099::6569e8a522e1770bd092f92c5953318f
https://hdl.handle.net/11368/2994373
https://hdl.handle.net/11368/2994373
Despite its large use, one major limitation of K-means algorithm is the impact of the initial seeding on the final partition. We propose a modified version, using the information contained in a co-association matrix obtained from clustering ensembles
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7b566540c463c31954869ff45ee82385
http://hdl.handle.net/11368/2946994
http://hdl.handle.net/11368/2946994
K-means algorithm is one of the most popular procedures in data clustering. Despite its large use, one major criticism is the impact of the initial seeding on the final solution. We propose a modification of the K-means algorithm, based on a suitable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::954a3c5ee77db6f7fcd2ea117309ed50
http://hdl.handle.net/11368/2929359
http://hdl.handle.net/11368/2929359
Publikováno v:
Studies in Classification, Data Analysis, and Knowledge Organization ISBN: 9783319557076
In many practical applications, the selection of copulas with a specific tail behaviour may allow to estimate properly the region of the distribution that is needed at most, especially in risk management procedures. Here, a graphical tool is presente
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c385971aa5f59b04fea326acd4448c2
https://hdl.handle.net/11368/2913303
https://hdl.handle.net/11368/2913303
Autor:
Roberta Pappadà, Francesco Pauli
Machine learning algorithms are routinely used for business decisions which may directly affect individuals: for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::073e6e397853f07b1cab455ba00ab472
http://hdl.handle.net/11368/2929374
http://hdl.handle.net/11368/2929374
Publikováno v:
Springer Proceedings in Mathematics & Statistics ISBN: 9783319739052
An algorithm for extracting identity submatrices of small rank and pivotal units from large and sparse matrices is proposed. The procedure has already been satisfactorily applied for solving the label switching problem in Bayesian mixture models. Her
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::530abab9797eca826e49efa0914bcebb
https://link.springer.com/chapter/10.1007/978-3-319-73906-9_7
https://link.springer.com/chapter/10.1007/978-3-319-73906-9_7