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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.
The results indicate that the average error of model training is small, the forecast effect is good, and it can satisfy the request of forecast precision that engineering practice to comminuting productive rate.
Extracting the most informative wavebands prior to model training is essential to avoid a curse of dimensionality; this is achieved by a new extended variant of forward selection, termed as forward selection with bands (FSB).
A more detailed description about model training is provided below.
Model training is covered, as well as transfer learning and fine-tuning.
The availability of relevant databases for model training is a critical point for ASR systems design.
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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.
Model training was based on training data only.
First, our primary biomarker discovery and prediction model training were performed by contrasting familial hypercholesterolemia patients against healthy controls.
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.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com