Advantages of edge-centric collective dynamics in machine learning tasks (2018)
- Authors:
- USP affiliated authors: LIANG, ZHAO - FFCLRP ; VERRI, FILIPE ALVES NETO - ICMC ; URIO, PAULO ROBERTO - ICMC
- Unidades: FFCLRP; ICMC
- DOI: 10.5890/jand.2018.09.005
- Subjects: PARTÍCULAS (FÍSICA NUCLEAR); APRENDIZADO COMPUTACIONAL; REDES NEURAIS
- Keywords: Complex networks; Nonlinear dynamics; Collective dynamics; Particle competition; Machine learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Glen Carbon
- Date published: 2018
- Source:
- Título do periódico: Journal of Applied Nonlinear Dynamics
- ISSN: 2164-6457
- Volume/Número/Paginação/Ano: v. 7, n. 3, p. 269-285, 2018
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
- Licença: other-oa
-
ABNT
VERRI, Filipe Alves Neto e URIO, Paulo Roberto e ZHAO, Liang. Advantages of edge-centric collective dynamics in machine learning tasks. Journal of Applied Nonlinear Dynamics, v. 7, n. 3, p. 269-285, 2018Tradução . . Disponível em: https://doi.org/10.5890/jand.2018.09.005. Acesso em: 05 ago. 2024. -
APA
Verri, F. A. N., Urio, P. R., & Zhao, L. (2018). Advantages of edge-centric collective dynamics in machine learning tasks. Journal of Applied Nonlinear Dynamics, 7( 3), 269-285. doi:10.5890/jand.2018.09.005 -
NLM
Verri FAN, Urio PR, Zhao L. Advantages of edge-centric collective dynamics in machine learning tasks [Internet]. Journal of Applied Nonlinear Dynamics. 2018 ; 7( 3): 269-285.[citado 2024 ago. 05 ] Available from: https://doi.org/10.5890/jand.2018.09.005 -
Vancouver
Verri FAN, Urio PR, Zhao L. Advantages of edge-centric collective dynamics in machine learning tasks [Internet]. Journal of Applied Nonlinear Dynamics. 2018 ; 7( 3): 269-285.[citado 2024 ago. 05 ] Available from: https://doi.org/10.5890/jand.2018.09.005 - Complex network component unfolding using a particle competition technique
- Collective dynamics in complex networks for machine learning
- Feature learning in feature–sample networks using multi-objective optimization
- Redes de elementos complexos para processamento de informação
- Structural outlier detection: a tourist walk approach
- Network-based high level data classification
- Uncovering overlapping structures via stochastic competitive learning
- Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning
- Enhancing weak signal transmission through a feedforward network
- Multiple images set classification via network modularity
Informações sobre o DOI: 10.5890/jand.2018.09.005 (Fonte: oaDOI API)
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