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The model distinguishes between running a learning instance and setting up a learning instance.
It could serve as a core model for how a learning instance is set up.
One advantage of the model is its grounding in pedagogical theories with the definition of HLIs that are used to set up a learning instance.
The five artifacts that are part of a learning instance could be used both for exploring potential standardization challenges, and for validation of existing projects.
SLE system sensors monitor each activity of a learning instance; the observations are fed back to a Context-awareness & Adaptiveness engine that adjusts the input factors for the next learning instantiation.
The elements of model can be described as follows: A Learning Instance, the key element to be observed in a SLE system, is a unit of learning that has activated the main activities represented in learning, which are accessible through HLIs.
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It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels.
An application to multi-label classification (in which each learning instance can belong to several classes simultaneously) is demonstrated.
Each sample represented one machine learning instance with gene expression values as features and 'pluripotent' or 'non-pluripotent' as class labels.
This kind of applications also does not count with many learning instances [41].
We repeat this 100 times, randomizing the instances in the learning and testing groups, and the order of the input learning instances.
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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