Stochastic competitive learning in complex networks (2012)
- Authors:
- Autor USP: LIANG, ZHAO - ICMC
- Unidade: ICMC
- DOI: 10.1109/TNNLS.2011.2181866
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE Computational Intelligence Society
- Publisher place: Los Alamitos
- Date published: 2012
- Source:
- Título do periódico: IEEE Transactions on Neural Networks and Learning Systems
- ISSN: 2162-237X
- Volume/Número/Paginação/Ano: v. 23, n. 3, p. 385-398, mar. 2012
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Thiago Christiano; LIANG, Zhao. Stochastic competitive learning in complex networks. IEEE Transactions on Neural Networks and Learning Systems, Los Alamitos, IEEE Computational Intelligence Society, v. 23, n. 3, p. 385-398, 2012. Disponível em: < http://dx.doi.org/10.1109/TNNLS.2011.2181866 > DOI: 10.1109/TNNLS.2011.2181866. -
APA
Silva, T. C., & Liang, Z. (2012). Stochastic competitive learning in complex networks. IEEE Transactions on Neural Networks and Learning Systems, 23( 3), 385-398. doi:10.1109/TNNLS.2011.2181866 -
NLM
Silva TC, Liang Z. Stochastic competitive learning in complex networks [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2012 ; 23( 3): 385-398.Available from: http://dx.doi.org/10.1109/TNNLS.2011.2181866 -
Vancouver
Silva TC, Liang Z. Stochastic competitive learning in complex networks [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2012 ; 23( 3): 385-398.Available from: http://dx.doi.org/10.1109/TNNLS.2011.2181866 - Redes de elementos complexos para processamento de informação
- Determining optimal structure of data raffic flow networks
- Uncovering overlapping structures via stochastic competitive learning
- Multiple images set classification via network modularity
- Enhancing weak signal transmission through a feedforward network
- Aprendizado de máquina em redes complexas
- Network-based high level data classification
- Particle competition and cooperation in networks for semi-supervised learning with concept drift
- Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning
- Classification of multiple observation sets via network modularity
Informações sobre o DOI: 10.1109/TNNLS.2011.2181866 (Fonte: oaDOI API)
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