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The function is learned through a training process [44].
Subsequently, a linear regression function is learned that maps appearance features represented by texton distributions to the roughness estimate.
In the standard PbD formulation of this problem, a parametrised function is learned, mapping from state, x t, which denotes the current position of the demonstrator's hand to x ̇ t, the hand's displacement.
In our previous work [21], we use a robust descriptor based on steerable filters to construct a reference template for each discrete pose of the training set, and a likelihood parametrized function is learned to match the templates to new entries.
Essentially, a new decision function is learned, defined as: (mathcal {J}^{solo}(x)=mathcal {J}^{RWC}(x)+Delta mathcal {J}(x)) where the new decision function follows a similar minimization procedure as a typical support vector machine classifier (Eq. 10) but with an added constrain to minimize the update term (Delta mathcal {J}(x)).
In TD models, the value function is learned
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Just as important as it is to learn the job function is learning about the culture.
Once the decision function is learnt from the training data, it can be used to predict the group membership of test individuals, case by case.
The final classifier and the kernel function are learned simultaneously which has the advantage of using labeled data during the kernel learning process.
In the subsequent section, this set of features was used to train a more complete model in which the weights for combining features in the scoring function are learned using the sampling approach described in Section 2.6.
An optimal set of parameters for defining the functions is learned from examples by minimizing an error functional.
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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