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• W2070403790 abstract "Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models." @default.
• W2070403790 created "2016-06-24" @default.
• W2070403790 creator A5042153931 @default.
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• W2070403790 date "2005-10-01" @default.
• W2070403790 modified "2023-10-17" @default.
• W2070403790 title "A mixture of experts network structure for modelling Doppler ultrasound blood flow signals" @default.
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• W2070403790 doi "https://doi.org/10.1016/j.compbiomed.2004.04.001" @default.
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