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pro vyhledávání: '"Lorena, Ana"'
Publikováno v:
International Journal of Public Sector Management, 2024, Vol. 37, Issue 6, pp. 824-841.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJPSM-11-2023-0329
Autor:
Torquette, Gustavo P., Nunes, Victor S., Paiva, Pedro Y. A., Neto, Lourenço B. C., Lorena, Ana C.
Some recent pieces of work in the Machine Learning (ML) literature have demonstrated the usefulness of assessing which observations are hardest to have their label predicted accurately. By identifying such instances, one may inspect whether they have
Externí odkaz:
http://arxiv.org/abs/2212.01897
Autor:
Duarte Soliani, Rodrigo, Vinicius Brito Lopes, Alisson, Santiago, Fábio, da Silva, Luiz Bueno, Emekwuru, Nwabueze, Carolina Lorena, Ana
Publikováno v:
In Journal of Safety Research February 2025 92:68-80
Several applications have a community structure where the nodes of the same community share similar attributes. Anomaly or outlier detection in networks is a relevant and widely studied research topic with applications in various domains. Despite a s
Externí odkaz:
http://arxiv.org/abs/2201.09936
For building successful Machine Learning (ML) systems, it is imperative to have high quality data and well tuned learning models. But how can one assess the quality of a given dataset? And how can the strengths and weaknesses of a model on a dataset
Externí odkaz:
http://arxiv.org/abs/2109.14430
Autor:
Lorena, Ana C.1 (AUTHOR) aclorena@ita.br, Paiva, Pedro Y. A.1 (AUTHOR) paiva@ita.br, Prudêncio, Ricardo B. C.2 (AUTHOR) rbcp@cin.ufpe.br
Publikováno v:
ACM Computing Surveys. Jul2024, Vol. 56 Issue 7, p1-28. 28p.
Akademický článek
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Autor:
Urbanetz, Lorena Ana Mercedes Lara, Junior, José Maria Soares, Maciel, Gustavo Arantes Rosa, Simões, Ricardo dos Santos, Baracat, Maria Cândida Pinheiro, Baracat, Edmund Chada
Publikováno v:
In Clinics January-December 2023 78
Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classification complexity can be used to estimate the difficulty in separatin
Externí odkaz:
http://arxiv.org/abs/1808.03591