I was reading this Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network and came across the below paragraph. Can you please help me understand what does the highlighted term
noise-tolerant learning or noisy-labeled training data` mean with a simple example and how is it useful when we don't have labels in our dataset etc? I am learning ML and your inputs would be helpful.
To address the scarcity of labeled training data, Chen et al used active learning to intelligently select training samples for labeling, demonstrating that classifier performance could be preserved with fewer samples.16 Another trend is the use of “silver standard training sets,” a semisupervised approach where training samples are labeled using an imperfect heuristic rather than by manual review.17–22 The intuition is that noise-tolerant classifiers trained on imperfectly labeled data will abstract higher order properties of the phenotype beyond the original labeling heuristic (so-called “noise-tolerant learning”23).