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Acoustic model training was done using the minimum phone error (MPE) [14] criteria.

The model training was done in an office environment, while in the SV testing phase, the audio signal was corrupted by a Gaussian additive noise.

As can be seen, the obtained correlation coefficient values were more than 0.99, demonstrating that the ANFIS model predicted the measured data satisfactorily, and that the neuro-fuzzy model training was successfully accomplished.

Model training was based on training data only.

The SVM based model training was done on the remaining 50% of both noncoding and coding RNA and performances were tested on the independent datasets.

In our study, model training was carried out using dEBV based on the 2007 genetic evaluation (dEBV2007), while dEBV based on the 2011 genetic evaluation (dEBV2011) were used for validation purposes.

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After the model training is finished, the test set data are input to the trained model for testing, and the experimental results are showed in Table 2.

A more detailed description about model training is provided below.

The availability of relevant databases for model training is a critical point for ASR systems design.

Note that the process of the model training is reproducible in spite of the randomness on noise injection and model initialization, since the random seed was hard-coded.

First, our primary biomarker discovery and prediction model training were performed by contrasting familial hypercholesterolemia patients against healthy controls.

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