Clustered echo state networks for signal observation and frequency filtering (2020)
- Authors:
- USP affiliated authors: LIANG, ZHAO - FFCLRP ; OLIVEIRA JUNIOR, LAERCIO DE - FFCLRP
- Unidade: FFCLRP
- DOI: 10.5753/kdmile.2020.11955
- Subjects: REDES COMPLEXAS; APRENDIZADO COMPUTACIONAL; REDES NEURAIS
- Language: Inglês
- Imprenta:
- Publisher place: Porto Alegre
- Date published: 2020
- Source:
- Título do periódico: Proceedings
- Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: bronze
-
ABNT
OLIVEIRA JUNIOR, Laercio de e STELZER, Florian e LIANG, Zhao. Clustered echo state networks for signal observation and frequency filtering. 2020, Anais.. Porto Alegre: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11955. Acesso em: 23 abr. 2024. -
APA
Oliveira Junior, L. de, Stelzer, F., & Liang, Z. (2020). Clustered echo state networks for signal observation and frequency filtering. In Proceedings. Porto Alegre: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.5753/kdmile.2020.11955 -
NLM
Oliveira Junior L de, Stelzer F, Liang Z. Clustered echo state networks for signal observation and frequency filtering [Internet]. Proceedings. 2020 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.5753/kdmile.2020.11955 -
Vancouver
Oliveira Junior L de, Stelzer F, Liang Z. Clustered echo state networks for signal observation and frequency filtering [Internet]. Proceedings. 2020 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.5753/kdmile.2020.11955 - Clustered Echo State networks for signal denoising and frequency filtering
- 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
- Classification of multiple observation sets via network modularity
- Particle competition and cooperation in networks for semi-supervised learning with concept drift
Informações sobre o DOI: 10.5753/kdmile.2020.11955 (Fonte: oaDOI API)
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