Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis (2018)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.patcog.2018.02.030
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE PADRÕES; ANÁLISE DE SÉRIES TEMPORAIS
- Keywords: Time series; Semi-supervised classification; Positive and unlabeled; Self-training; Phase space; Cross-recurrence quantification analysis
- Language: Inglês
- Imprenta:
- Publisher place: Kidlington
- Date published: 2018
- Source:
- Título: Pattern Recognition
- ISSN: 0031-3203
- Volume/Número/Paginação/Ano: v. 80, p. 53-63, Aug. 2018
- Este artigo NÃO possui versão em acesso aberto
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Status: Nenhuma versão em acesso aberto identificada -
ABNT
PAGLIOSA, Lucas de Carvalho e MELLO, Rodrigo Fernandes de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis. Pattern Recognition, v. 80, p. 53-63, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.patcog.2018.02.030. Acesso em: 10 mar. 2026. -
APA
Pagliosa, L. de C., & Mello, R. F. de. (2018). Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis. Pattern Recognition, 80, 53-63. doi:10.1016/j.patcog.2018.02.030 -
NLM
Pagliosa L de C, Mello RF de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis [Internet]. Pattern Recognition. 2018 ; 80 53-63.[citado 2026 mar. 10 ] Available from: https://doi.org/10.1016/j.patcog.2018.02.030 -
Vancouver
Pagliosa L de C, Mello RF de. Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis [Internet]. Pattern Recognition. 2018 ; 80 53-63.[citado 2026 mar. 10 ] Available from: https://doi.org/10.1016/j.patcog.2018.02.030 - A novel approach to quantify novelty levels applied on ubiquitous music distribution
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