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first-hit (see Definition 2) and label disclosure complexity (see Definition 3), evaluated once for every combination of dataset, base classifier and query selection criterion.
These objectives will be assessed from several performance indicators: error and known classes (see Definition 1), evaluated at each iteration throughout the learning cycle and first-hit (see Definition 2) and label disclosure complexity (see Definition 3), evaluated once for every combination of dataset, base classifier and query selection criterion.
Labeling disclosure regulation would fall under the generally GMO-friendly U.S. Department of Agriculture USDAA), not the more health-conscience Food and Drug Administration FDAA), which already regulates labeling food ingredients.
The fast-growing energy drink industry is facing increasing scrutiny over issues like labeling disclosures and possible health risks.
D-Confidence consistently presents lower label disclosure complexity irrespectively of the base classifier.
Our evaluation will be based on the performance indicators referred above: error, known classes, first-hit and label disclosure complexity.
Label disclosure complexity (LDC) is the minimum number of queries that are required to identify at least one instance from every c k ∈C.
Label disclosure complexity (LDC) aims to evaluate the ability of the learning process to reveal all the classes belonging to the concept to learn.
We provide empirical evidence that d-Confidence reduces label disclosure complexity which we have defined as the number of queries required to identify instances from all classes to learn when in the presence of imbalanced data.
Label disclosure of all ingredients with percent composition would facilitate these much-needed studies.
[T]here is a long history of government-enacted labeling disclosures that have nothing to do with safety concerns.
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CEO of Professional Science Editing for Scientists @ prosciediting.com