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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) aims to evaluate the ability of the learning process to reveal all the classes belonging to the concept to learn.
Label disclosure complexity (LDC) is the minimum number of queries that are required to identify at least one instance from every c k ∈C.
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.
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.
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Yet now the dairy industry is getting a hearing on its 2009 citizen's petition (PDF linked here) asking FDA for permission to abandon those front label disclosures for artificially dairy sweetened products -- and not just on flavored milk, but also on seventeen other dairy products having nothing to do with schools, including whipping cream, sour cream, nonfat dry mik and more.
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.
[T]here is a long history of government-enacted labeling disclosures that have nothing to do with safety concerns.
But beyond this imbalance, he entirely misses the fact that there 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