Nature-inspired graph optimization for dimensionality reduction (2017)
- Authors:
- Autor USP: LIANG, ZHAO - FFCLRP
- Unidade: FFCLRP
- Subjects: APRENDIZADO COMPUTACIONAL; INTELIGÊNCIA ARTIFICIAL
- Keywords: Graph-based dimensionality reduction; Nature-inspired graph optimization; Graph-based machine learning
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: International Conference on Tools with Artficial Intelligence
-
ABNT
CARNEIRO, Murillo G. et al. Nature-inspired graph optimization for dimensionality reduction. 2017, Anais.. Boston: IEEE, 2017. . Acesso em: 13 out. 2024. -
APA
Carneiro, M. G., Cupertino, T. H., Cheng, R., Jin, Y., & Liang, Z. (2017). Nature-inspired graph optimization for dimensionality reduction. In Proceedings. Boston: IEEE. -
NLM
Carneiro MG, Cupertino TH, Cheng R, Jin Y, Liang Z. Nature-inspired graph optimization for dimensionality reduction. Proceedings. 2017 ;[citado 2024 out. 13 ] -
Vancouver
Carneiro MG, Cupertino TH, Cheng R, Jin Y, Liang Z. Nature-inspired graph optimization for dimensionality reduction. Proceedings. 2017 ;[citado 2024 out. 13 ] - 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
- Aprendizado de máquina em redes complexas
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas