Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning (2013)
- Authors:
- Autor USP: LIANG, ZHAO - FFCLRP
- Unidade: FFCLRP
- Subjects: APRENDIZADO COMPUTACIONAL; COMPUTAÇÃO GRÁFICA; REDES COMPLEXAS
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: BRICS Contries Congress
-
ABNT
ARAÚJO, Bilzã e LIANG, Zhao. Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning. 2013, Anais.. Ipojuca: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2013. . Acesso em: 13 mar. 2026. -
APA
Araújo, B., & Liang, Z. (2013). Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning. In Proceedings. Ipojuca: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. -
NLM
Araújo B, Liang Z. Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning. Proceedings. 2013 ;[citado 2026 mar. 13 ] -
Vancouver
Araújo B, Liang Z. Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning. Proceedings. 2013 ;[citado 2026 mar. 13 ] - Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
- Computer-aided music composition with LSTM neural network and chaotic inspiration
- Semi-supervised learning by edge domination in complex networks
- Dimensionality reduction with the k-associated optimal graph applied to image classification
- Bias-guided random walk for network-based data classification
- A review and comparative analysis of coarsening algorithms on bipartite networks
- Semi-supervised learning from imperfect data through particle cooperation and competition
- Chaotic phase synchronization and desynchronization in an oscillator network for object selection
- Random walk in feature-sample networks for semi-supervised classification
- Classificação de alto nível baseada em entropia da rede
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
