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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.
The wavelet coefficients and power spectral density (PSD) values obtained by eigenvector methods were used as features, and these features were fed to each of the seven classification algorithms (SVM, recurrent neural networks (RNN), PNN, mixture of experts (ME), modified mixture of experts (MME), combined neural networks (CNN), and multilayer perceptron neural network (MLPNN)).
A modified mixture of Gaussian (MOG) algorithm is used.
This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of Doppler ultrasound signals with diverse features.
This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features.
This is done by a modified mixture of experts architecture, where each expert is linear and more than a single expert may be assigned to the same input region.
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In order to address the impact of perturbation on state estimation, an adaptive estimator based on modified mixture-of-expert framework is given.
In brief, mRNAs were synthesized by in vitro transcription of template DNAs encoding mouse Zscan4c, human ZSCAN4, or Green Fluorescent Protein (GFP) with modified mixtures of dNTP to increase RNA stability as well as translation efficiency in mammalian cells.
The data was used to compare the performance of the fiber modified mixture to the control.
Modification was performed with the PMPS/DMC mixture with the different ratios of polymer and initiator (3.4, 5.8, 20.0) (Table 1) and amount of the modified mixture at each stage (Table 1).
Finally, the effects of these factors on anti-icing performance of modified mixture were investigated.
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