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The parameters of this model are learned from a set of demonstrations performed by a human.
The parameters of this model are learned using the maximum likelihood (ML) principle [22].
Parameters of the model are learned from a corpus annotated with coreference relations and the requisite syntactic analyses.
The parameters for each model are learned in an unsupervised fashion as the robot experiments with its arm over a period of four minutes.
Multivariate statistical methods, and in particular classification techniques, may perform excellently on the training data where the parameters of the model are learned.
We model this using stochastic finite state machines, where the parameters of the model are learned from a corpus of human conversations.
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In parallel, a nominal model was learned from vehicles which complied with the traffic rules.
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.
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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