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The phasing model is learnt using an analysis-synthesis loop that iterates HMM estimations and forced alignments with the original data.
Via these constructed attribute features, a predictive model is learnt by training a classifier using annotated proteins, and then utilize this model to predict the functions of the proteins [ 2- 5].
For each activity, a model is learnt and stored as an exemplar.
With this cumulative hidden layer, our model is learnt indirectly using faces with neighbouring ages and thus alleviate the sample imbalance problem.
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The inference model is learned from the on-the-shelf training images without any occlusion.
A similar approach was proposed by Khan et al. [62], where the local model is learned from the facial region.
Then, a BoW model is learned by clustering the set of all covariance matrices from training video activities.
The codebook of this model is learned by clustering per-frame covariance matrices from all videos in the training set.
Our generative model is learned using silhouettes from a set of targets of different classes observed from multiple viewpoints.
A new segmentation is obtained when the aspect model is learned and this process iterates until the final segmentation is obtained.
First, meaningful features need to be extracted, and second, a classification model is learned which maps the feature space to the instrument categories.
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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