A scheme for high level data classification using random walk and network measures (2018)
- Authors:
- Autor USP: LIANG, ZHAO - FFCLRP
- Unidade: FFCLRP
- DOI: 10.1016/j.eswa.2017.09.014
- Subjects: APRENDIZADO COMPUTACIONAL; GESTÃO DA INFORMAÇÃO; PASSEIOS ALEATÓRIOS
- Keywords: SUPERVISED LEARNING; DATA CLASSIFICATION; NETWORK-BASED LEARNING; HIGH LEVEL CLASSIFICATION; MARKOV CHAIN; RANDOM WALK; LIMITING PROBABILITIES; STEADY STATES
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Expert Systems with Applications
- ISSN: 0957-4174
- Volume/Número/Paginação/Ano: v. 92, p. 289-303, 2018
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CUPERTINO, Thiago Henrique et al. A scheme for high level data classification using random walk and network measures. Expert Systems with Applications, v. 92, p. 289-303, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2017.09.014. Acesso em: 18 abr. 2024. -
APA
Cupertino, T. H., Carneiro, M. G., Qiusheng, Z., Junbao, Z., & Liang, Z. (2018). A scheme for high level data classification using random walk and network measures. Expert Systems with Applications, 92, 289-303. doi:10.1016/j.eswa.2017.09.014 -
NLM
Cupertino TH, Carneiro MG, Qiusheng Z, Junbao Z, Liang Z. A scheme for high level data classification using random walk and network measures [Internet]. Expert Systems with Applications. 2018 ; 92 289-303.[citado 2024 abr. 18 ] Available from: https://doi.org/10.1016/j.eswa.2017.09.014 -
Vancouver
Cupertino TH, Carneiro MG, Qiusheng Z, Junbao Z, Liang Z. A scheme for high level data classification using random walk and network measures [Internet]. Expert Systems with Applications. 2018 ; 92 289-303.[citado 2024 abr. 18 ] Available from: https://doi.org/10.1016/j.eswa.2017.09.014 - 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
Informações sobre o DOI: 10.1016/j.eswa.2017.09.014 (Fonte: oaDOI API)
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