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Parameters were trained based on the predicted genes from searching homologous and high-confidence transcriptomes.
Various NN structures were trained based on the back-propagation feed-forward approach.
In prediction step, three artificial neural networks for concentration prediction of three analytes were trained based on an error back-propagation algorithm.
The DNNs reported in this section were all initialized from scratch and were trained based on the same alignments provided by the LDA+MLLT GMM system.
A variety of computational models were trained based on the different types or combinations of features.
All models were trained based on the observed information as feature inputs.
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Three layer neural networks were trained basing on selected data from numerical investigations on hard machining of 52100 bearing steel, and then validated with data obtained by experiments.
For each sequence, an AAM was trained based on exactly one stride, using the provided landmark data.
As an illustration, for the traditional classification-based QSAR models, they are trained based on a set of molecules with known classification labels for a given target.
Since QRNN is trained based on temporally simultaneous features, it cannot be utilized directly in forecasting one year ahead because some features, like hourly temperature in the next year, cannot be foreseen.
Two artificial neural networks (ANN) are trained based on the response data simulated using finite element method (FEM) and perturbation theory enhanced finite element method (PFEM), respectively.
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