Active metric learning for supervised classification

Kumaran, K.; Papageorgiou, Dimitri J.; Takáč, Martin; Lueg, Laurens; Sahinidis, Nicolas V

doi:10.1016/j.compchemeng.2020.107132

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In the split stage, the Hadoop default file block strategy is used to divide the original data into blocks of the same size; in the Map stage, the Map function is used to calculate the local change of each network weight parameter, update the weights, and obtain partial classification results. Since the stochastic gradient descent method of updating weights in the Map stage converges very slowly in the big data environment, based on the secondary convergence of the conjugate gradient method, this paper proposes a modified secant line of the conjugate gradient method (CGMSE) to find the optimal parameters and speed up the network convergence [23,24].…”

Section: Obtaining Local Classificationmentioning

confidence: 99%

[Retracted] Using Big Data‐Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

Sun

2021

Computational Intelligence and Neuroscience

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In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm’s research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.

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Section: Obtaining Local Classificationmentioning

confidence: 99%

[Retracted] Using Big Data‐Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

Sun

2021

Computational Intelligence and Neuroscience

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“…Em [Bressan et al 2019], a utilização de Support Vector Machine Active com RR define um hiperplano separador entre os elementos rotulados como relevantes em imagens de mamografias. No trabalho de [Kumaran et al 2021], o uso de aprendizado de métrica consistiu em utilizar a aprendizagem ativa para selecionar exemplos relevantes para atualizar a matriz de Mahalanobis durante o treinamento e calcular a distância entre as amostras para classificação.…”

Section: Trabalhos Relacionadosunclassified

Calibração de Distância em Métodos de Acesso Métrico por meio de Realimentação de Relevância

Marcacini,

de Oliveira,

Traina

2023

Anais Do XXXVIII Simpósio Brasileiro De Banco De Dados (SBBD 2023)

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Usualmente, os Métodos de Acesso Métrico (MAM) utilizam funções de distância fixas para realizar a construção da árvore métrica, o que por sua vez impede que um MAM consiga indexar os elementos utilizando duas ou mais funções de distância na mesma indexação. Um vetor de pesos corretamente aprendido por Realimentação de Relevância (RR), permite ponderar funções de distâncias e aprimorar a semântica dos dados, trazendo maior precisão ao processamento de consultas. Este trabalho apresenta a abordagem denominada Tuning Metrics Relevance Feedback (TMRF), que incluem funções de distâncias ponderadas no MAM Slim-Tree, mantendo-a eficiente, sendo 70% mais rápida em relação a estratégias sequenciais, com ganhos expressivos em termos de acurácia de até 42% através de aprendizado por RR.

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Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling

Wang

Zheng

Liang

et al. 2022

Computers & Chemical Engineering

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Active metric learning for supervised classification

Cited by 4 publications

References 14 publications

[Retracted] Using Big Data‐Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

[Retracted] Using Big Data‐Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

Calibração de Distância em Métodos de Acesso Métrico por meio de Realimentação de Relevância

Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling

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