Robustness analysis of network-based semi-supervised learning algorithms (2012)
- Authors:
- Autor USP: LIANG, ZHAO - ICMC
- Unidade: ICMC
- DOI: 10.1109/SBRN.2012.47
- Subjects: INTELIGÊNCIA ARTIFICIAL; SISTEMAS DINÂMICOS
- Language: Inglês
- Imprenta:
- Publisher: CPS
- Publisher place: Piscataway
- Date published: 2012
- ISBN: 9780769548234
- Source:
- Título: Proceedings
- Conference titles: Brazilian Conference on Neural Networks - SBRN
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ARAÚJO, Bilzã e LIANG, Zhao. Robustness analysis of network-based semi-supervised learning algorithms. 2012, Anais.. Piscataway: CPS, 2012. Disponível em: https://doi.org/10.1109/SBRN.2012.47. Acesso em: 18 nov. 2024. -
APA
Araújo, B., & Liang, Z. (2012). Robustness analysis of network-based semi-supervised learning algorithms. In Proceedings. Piscataway: CPS. doi:10.1109/SBRN.2012.47 -
NLM
Araújo B, Liang Z. Robustness analysis of network-based semi-supervised learning algorithms [Internet]. Proceedings. 2012 ;[citado 2024 nov. 18 ] Available from: https://doi.org/10.1109/SBRN.2012.47 -
Vancouver
Araújo B, Liang Z. Robustness analysis of network-based semi-supervised learning algorithms [Internet]. Proceedings. 2012 ;[citado 2024 nov. 18 ] Available from: https://doi.org/10.1109/SBRN.2012.47 - Redes de elementos complexos para processamento de informação
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- 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
- Classification of multiple observation sets via network modularity
- Particle competition and cooperation in networks for semi-supervised learning with concept drift
- Aprendizado de máquina em redes complexas
Informações sobre o DOI: 10.1109/SBRN.2012.47 (Fonte: oaDOI API)
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