Imputation of missing data supported by complete p-partite attribute-based decision graphs (2014)
- Authors:
- Autor USP: LIANG, ZHAO - FFCLRP
- Unidade: FFCLRP
- Subjects: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; REDES NEURAIS; COMPUTAÇÃO BIOINSPIRADA
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: International Joint Conference on Neural Networks (IJCNN)
-
ABNT
BERTINI JÚNIOR, João Roberto e NICOLETTI, Maria do Carmo e LIANG, Zhao. Imputation of missing data supported by complete p-partite attribute-based decision graphs. 2014, Anais.. Beijing: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2014. . Acesso em: 23 jan. 2026. -
APA
Bertini Júnior, J. R., Nicoletti, M. do C., & Liang, Z. (2014). Imputation of missing data supported by complete p-partite attribute-based decision graphs. In Proceedings. Beijing: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. -
NLM
Bertini Júnior JR, Nicoletti M do C, Liang Z. Imputation of missing data supported by complete p-partite attribute-based decision graphs. Proceedings. 2014 ;[citado 2026 jan. 23 ] -
Vancouver
Bertini Júnior JR, Nicoletti M do C, Liang Z. Imputation of missing data supported by complete p-partite attribute-based decision graphs. Proceedings. 2014 ;[citado 2026 jan. 23 ] - Fractal color image compression
- Network-based stochastic semisupervised learning
- K-associated optimal network for graph embedding dimensionality reduction
- Tunable interactions in starch-anthocyanin complexes switched by high hydrostatic pressure
- Chaotic synchronization for scene segmentation
- Musical scales recognition via deterministic walk in a graph
- Phase-noise-induced resonance in arrays of coupled excitable neural models
- Uncovering overlapping structures via stochastic competitive learning
- Selecting nodes with inhomogeneous centrality profile for labeling for network-based semi-supervised learning
- A scheme for high level data classification using random walk and network measures
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
