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Three models are created for the purpose of evaluation, one is trained on the training data, the second is trained on the development dataset, and the third is trained on both training and development datasets.
The model is trained on n sequences, one half labeled as foreground, the other background, sampled from the reference genome.
Each classifier is trained on a data set with one sample of the tumor type withheld.
In each round of LOOCV, one sample is left out and R-SVM is trained on the remaining samples.
Fgenesh is trained on sequences of Penicillium funiculosum and GeneMark-ES is upon the self-training algorithm for fungal genomes.
Each classifier is trained on n-1 of the total n training samples and tested on the one left out.
A separate spotlight is trained on each bed.
That lens, often, is trained on a mirror.
The CCTV camera on "the bridge", the raised custody sergeant's desk, is trained on cell M1.
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The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate.
Each discrete-time high order neural network is trained on-line with an extended Kalman filter based algorithm.
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