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Mixture of experts (ME) and modified mixture of experts (MME) architectures were formulated and used as basis for detection of arterial disorders.
We review the studies on a mixture of experts closely related to our work.
The similarity level fusion is based on the strategy known as mixture of experts.
Mixture of experts (ME) is a modular neural network architecture for supervised learning.
Mixture of experts (ME) is modular neural network architecture for supervised learning.
Lastly, we propose a mixture of experts (ME) approach, which is based on the divide-and-conquer principle.
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This dynamic weighting is closely related to gating in a mixture-of-experts architecture.
Furthermore, a hierarchical motion controller is proposed by dividing the controller into multiple sub-controllers using a mixture-of-experts framework to further alleviate the computational cost.
This paper investigates the credit scoring accuracy of five neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance.
West 2000 used five neural network models: multi-layer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance for individual credit.
Results demonstrate that the multilayer perceptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications.
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