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where j is the image feature label and j=1…n.
Case-2 Predict new product tendency: Objective feature label table lists new product tendency prediction and associated classification of the products in the top customer records as feature label.
For a feature label or a set of feature labels, the fusion planning engine is to discover and schedule a sequence of algorithms in the algorithm pool triggered by the feature label(s).
Case-1 Awarding top customers: Objective feature label table lists top/best customers and associated customer value, customer segmentation and customer scoring as feature labels.
Denoting this feature label by χ then all of these models are expressed in terms of some non-local integro-differential equation for u ( r, χ, t ).
However, sorted_instances is not contained in the Dataset pool but it as a feature label triggers the algorithm, RFM_score_sorting in the Algorithm pool.
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A previous version of this interactive feature labeled a group as "Defund Democrats" and incorrectly suggested the Democrats had all voted for defunding or repealing the act.
Such feature labeling eliminates noise from the initial training set, resulting in a significant increase of precision at the expense of recall.
As show in Fig. 4, its associated two feature labels are top_instances and customer_value.
Moreover, experiments in [22] examine the effect of allowing end users to do feature labeling, instead of annotating instances of training data: especially for small amounts of training, the feature labeling approach was shown to be effective.
In our work, we do not incorporate feature labeling, but we will consider it in our future work.
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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