Random walk in feature-sample networks for semi-supervised classification (2016)
- Authors:
- USP affiliated author: LIANG, ZHAO - FFCLRP
- School: FFCLRP
- DOI: 10.1109/bracis.2016.051
- Subjects: PASSEIOS ALEATÓRIOS; REDES COMPLEXAS
- Keywords: SEMI-SUPERVISED CLASSIFICATION; COMPLEX NETWORKS; POSITIVE-UNLABELED LEARNING; RANDOM WALK
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Annals
- Conference title: Brazilian Conference on Intelligent Systems (BRACIS)
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
VERRI, Filipe Alves Neto e LIANG, Zhao. Random walk in feature-sample networks for semi-supervised classification. 2016, Anais.. Recife: IEEE, 2016. Disponível em: http://dx.doi.org/10.1109/bracis.2016.051. Acesso em: 13 ago. 2022. -
APA
Verri, F. A. N., & Liang, Z. (2016). Random walk in feature-sample networks for semi-supervised classification. In Annals. Recife: IEEE. doi:10.1109/bracis.2016.051 -
NLM
Verri FAN, Liang Z. Random walk in feature-sample networks for semi-supervised classification [Internet]. Annals. 2016 ;[citado 2022 ago. 13 ] Available from: http://dx.doi.org/10.1109/bracis.2016.051 -
Vancouver
Verri FAN, Liang Z. Random walk in feature-sample networks for semi-supervised classification [Internet]. Annals. 2016 ;[citado 2022 ago. 13 ] Available from: http://dx.doi.org/10.1109/bracis.2016.051 - Reconhecimento de padrões multi-valor por redes neurais caóticas
- Lattice synchronization of neural oscillators for scene segmentation
- Scene segmentation of the chaotic oscillator network
- Network-based learning through particle competition for data clustering
- Organizational data classification based on the importance concept of complex networks
- Semi-supervised learning in complex networks
- High level classification for pattern recognition
- Metodologia baseada em redes complexas para análise das votações de deputados brasileiros
- A biologically motivated paradigm for scene segmentation
- QK-means: a clustering technique based on community detection and 'capa'-means for deployment of custer head nodes
Informações sobre o DOI: 10.1109/bracis.2016.051 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas