Using cross-recurrence quantification analysis to improve semi-supervised time series classification of positive and unlabeled problems (2017)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS
- Language: Inglês
- Imprenta:
- Source:
- Título: Scientific Programme
- Conference titles: International Symposium on Recurrence Plots
-
ABNT
PAGLIOSA, Lucas e MELLO, Rodrigo Fernandes de. Using cross-recurrence quantification analysis to improve semi-supervised time series classification of positive and unlabeled problems. 2017, Anais.. São Paulo: Poli/USP, 2017. Disponível em: http://symposium.recurrence-plot.tk/programme2017.pdf. Acesso em: 27 jan. 2026. -
APA
Pagliosa, L., & Mello, R. F. de. (2017). Using cross-recurrence quantification analysis to improve semi-supervised time series classification of positive and unlabeled problems. In Scientific Programme. São Paulo: Poli/USP. Recuperado de http://symposium.recurrence-plot.tk/programme2017.pdf -
NLM
Pagliosa L, Mello RF de. Using cross-recurrence quantification analysis to improve semi-supervised time series classification of positive and unlabeled problems [Internet]. Scientific Programme. 2017 ;[citado 2026 jan. 27 ] Available from: http://symposium.recurrence-plot.tk/programme2017.pdf -
Vancouver
Pagliosa L, Mello RF de. Using cross-recurrence quantification analysis to improve semi-supervised time series classification of positive and unlabeled problems [Internet]. Scientific Programme. 2017 ;[citado 2026 jan. 27 ] Available from: http://symposium.recurrence-plot.tk/programme2017.pdf - Improving performance of information quality applications: a scale-out approach
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