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pro vyhledávání: '"Alessandro Albano"'
Label Ranking (LR) is a non-standard supervised classification method with the aim of ranking a finite collection of labels according to a set of predictor variables. Traditional LR models assume indifference among alternatives. However, misassigning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::2bdaf504f0b72da82dd08e25a5d04c40
https://hdl.handle.net/10447/597517
https://hdl.handle.net/10447/597517
Autor:
Mariangela Sciandra, Alessandro Albano
Probabilistic topic models, such as LDA, are standard text analysis algorithms that provide predictive and latent topic representation for a corpus. However, due to the unsupervised training process, it is difficult to verify the assumption that the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::d3d3ec8a5428ef4dfdd2c7ab381173b5
http://hdl.handle.net/10447/564283
http://hdl.handle.net/10447/564283
Probabilistic topic models have become one of the most widespread machine learning technique for textual analysis purpose. In this framework, Latent Dirichlet Allocation (LDA) gained more and more popularity as a text modelling technique. The idea is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::d37c85d1fae3a92e2f2f63e1e856e82c
http://hdl.handle.net/10447/531495
http://hdl.handle.net/10447/531495
Autor:
Alessandro Albano, Andrea Simonetti
Probabilistic topic models have become one of the most widespread machine learning technique for textual analysis purpose. In this framework, Latent Dirichlet Allocation (LDA) (Blei et al., 2003) gained more and more popularity as a text modelling te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::4a9e2e0f32fe50d73696505c8ae87616
http://hdl.handle.net/10447/541515
http://hdl.handle.net/10447/541515
Label Ranking (LR), an emerging non-standard supervised classification problem, aims at training preference models that order a finite set of labels based on a set of predictor features. Traditional LR models regard all labels as equally important. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::3131ab06e0b6ca417f44793f6cc7d1c6
http://hdl.handle.net/10447/567283
http://hdl.handle.net/10447/567283
The task of combining preference rankings and approval voting is a relevant issue in social choice theory. The preference-approval voting (PAV) analyses the preferences of a group of individuals over a set of items. The main difference with the class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::91bebe44443ef93d99b92be6f350fe9b
http://hdl.handle.net/10447/568803
http://hdl.handle.net/10447/568803
Probabilistic topic models are machine learning tools for processing and understanding large text document collections. Among the different models in the literature, Latent Dirichlet Allocation (LDA) has turned out to be the benchmark of the topic mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::e7f37998cd1d26fbbf3f54a2fc313abc
https://hdl.handle.net/10447/576096
https://hdl.handle.net/10447/576096
Publikováno v:
Journal of Information Science. :016555152211483
Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluatio
The outbreak of coronavirus disease 2019 (COVID-19) was highly stressful for people. In general, fear and anxiety about a disease can be overwhelming and cause strong emotions in adults and children. One way to cope with this stress consists in liste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::153a3ac3fd3238a2f25adfd869e2271e
http://hdl.handle.net/10447/524502
http://hdl.handle.net/10447/524502
Publikováno v:
Expert Systems with Applications. 213:119000
Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a