Semi-Supervised classification by particle competition in complex network’s edges (2016)
- Authors:
- Autor USP: LIANG, ZHAO - FFCLRP
- Unidade: FFCLRP
- DOI: 10.1142/S0218001416600065
- Subjects: REDES COMPLEXAS; SISTEMAS DINÂMICOS (FÍSICA MATEMÁTICA); PARTÍCULAS (FÍSICA NUCLEAR); APRENDIZADO COMPUTACIONAL
- Keywords: Complex networks; Nonlinear dynamical systems; Particle competition; Semi-supervised learning
- Language: Inglês
- Imprenta:
- Source:
- Título: International Journal of Pattern Recognition and Artificial Intelligence
- ISSN: 0218-0014
- Volume/Número/Paginação/Ano: v. 30, n. 9, art. 1660006, 2016
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
URIO, Paulo Roberto e VERRI, Filipe Alves Neto e LIANG, Zhao. Semi-Supervised classification by particle competition in complex network’s edges. International Journal of Pattern Recognition and Artificial Intelligence, v. 30, n. 9, 2016Tradução . . Disponível em: https://doi.org/10.1142/S0218001416600065. Acesso em: 03 mar. 2026. -
APA
Urio, P. R., Verri, F. A. N., & Liang, Z. (2016). Semi-Supervised classification by particle competition in complex network’s edges. International Journal of Pattern Recognition and Artificial Intelligence, 30( 9). doi:10.1142/S0218001416600065 -
NLM
Urio PR, Verri FAN, Liang Z. Semi-Supervised classification by particle competition in complex network’s edges [Internet]. International Journal of Pattern Recognition and Artificial Intelligence. 2016 ; 30( 9):[citado 2026 mar. 03 ] Available from: https://doi.org/10.1142/S0218001416600065 -
Vancouver
Urio PR, Verri FAN, Liang Z. Semi-Supervised classification by particle competition in complex network’s edges [Internet]. International Journal of Pattern Recognition and Artificial Intelligence. 2016 ; 30( 9):[citado 2026 mar. 03 ] Available from: https://doi.org/10.1142/S0218001416600065 - Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
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Informações sobre o DOI: 10.1142/S0218001416600065 (Fonte: oaDOI API)
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