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Active learning approaches [13, 33, 36] reduce label complexity by analyzing unlabeled instances and selecting the most useful ones once labeled.
In our case, due to the significant biological variations existing among blueberry samples, the classifier required frequent updates in practical applications, and the active learning algorithms could remarkably reduce label effort during the model updating processes.
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Compared with tissue incubated in the primary antibody alone (Fig. 3D), tissue preabsorbed in aldosterone did not reduce labeling (Fig. 3E), providing further evidence of specificity of the antibodies.
Globin transcripts, which reduce labeling efficiency of whole blood cell RNA and decrease signal-to-noise ratios on microarrays (14), were reduced using GlobinClear (human; Ambion).
Globin transcripts, which reduce labeling efficiency of whole blood cell RNA and decrease signal-to-noise ratios on microarrays [ 25] were reduced using GLOBINclear-Human (Ambion, Austin, TX, USA).
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.
Introduction of G18A and U54C mutations, which disrupts the interaction between the D and TΨC loops, reduced label transfer efficiency.
In special cases, reduced labeling degree of the tracer substrate can increase the precision of flux analysis.
GL+LpcSVM (with and without noise n k in p k ) demonstrates comparable classification accuracy and Kappa coefficient with PL+SVM, while reducing labeling cost significantly by adopting grid labeling.
The most essential factors for attaining high efficacy of targeted immunotherapy of cancers is its specificity and sensitivity in targeting the receptors on cancer cells, while eliminating, or at least reducing, labeling of healthy cells (Fig. 2).
24-hr exposure to MβCD visibly reduced labeling intensity.
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