scholarly journals A Comparative Evaluation Study of Automated Gait Recognition based on Spatiotemporal Feature and Different Neural Network Classifiers

2013 ◽  
Vol 68 (9) ◽  
pp. 36-42
Author(s):  
Noha A.Hikal
Keyword(s):  
Neural Network ◽  
Comparative Evaluation ◽  
Gait Recognition ◽  
Evaluation Study ◽  
Spatiotemporal Feature ◽  
Neural Network Classifiers

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

scholarly journals Neural network classifiers for images of genetic conditions with cutaneous manifestations

2021 ◽  
pp. 100053 ◽  
Author(s):  
Dat Duong ◽  
Rebekah L. Waikel ◽  
Ping Hu ◽  
Cedrik Tekendo-Ngongang ◽  
Benjamin D. Solomon
Keyword(s):  
Neural Network ◽  
Cutaneous Manifestations ◽  
Neural Network Classifiers
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