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pro vyhledávání: '"Greselin, F."'
Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering - at the same time - dimension reduction and model-based clustering. Unfortunately, the high prevalence of spurious solutions and the distur
Externí odkaz:
http://arxiv.org/abs/1503.06302
A robust estimator for a wide family of mixtures of linear regression is presented. Robustness is based on the joint adoption of the Cluster Weighted Model and of an estimator based on trimming and restrictions. The selected model provides the condit
Externí odkaz:
http://arxiv.org/abs/1502.01118
Background: The General Health Questionnaire (GHQ) is a widely used tool in clinical and research settings due to its brevity and easy administration. Researchers often adopt a dichotomous measurement method, considering a total score above or below
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::b2476f9d66c881c2767f0885b78554c7
https://hdl.handle.net/10281/404035
https://hdl.handle.net/10281/404035
Akademický článek
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Depending on the selected hyper-parameters, cluster weighted modeling may produce a set of diverse solutions. Particularly, the user can manually specify the number of mixture components, the degree of heteroscedasticity of the clusters in the explan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::344ec49817e6df354280208cc387f77f
http://hdl.handle.net/10281/335955
http://hdl.handle.net/10281/335955
This special issue of Statistical Analysis and Data Mining collects papers presented at the 12-th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Cassino, Italy, September 11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3730::d0db0770ca6c16c37d544f82560d4de9
http://hdl.handle.net/11588/858334
http://hdl.handle.net/11588/858334
In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::7ee23d6e5bf80dbe15063e5786c9735f
http://hdl.handle.net/10281/290334
http://hdl.handle.net/10281/290334
Several contributions to the recent literature have shown that supervised learning is greatly enhanced when only the most relevant features are selected for building the discrimination rule. Unfortunately, outliers and wrongly labelled units may unde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::041395d484094951cf675f09f8ce2910
http://hdl.handle.net/10281/290338
http://hdl.handle.net/10281/290338
Three important issues are often encountered in Supervised Classification: class-memberships are unreliable for some training units (Label Noise), a proportion of observations might depart from the bulk of the data structure (Outliers) and groups rep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::a8c9a3c00a81e9131f9b2f09e07b8a77
http://hdl.handle.net/10281/257199
http://hdl.handle.net/10281/257199
Negli studi di autenticità degli alimenti risulta cruciale saper riconoscere prodotti contraffatti. In questo paper si adotta un approccio robusto per modificare una regola di classificazione semi-supervised e poter quindi identificare potenziali ad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1299::804e6d56d28de403944caec806b89e82
http://hdl.handle.net/10281/206311
http://hdl.handle.net/10281/206311