Selective Sampling is a technique in machine learning where the learning algorithm selectively queries labels for specific instances from a large pool of unlabeled data, usually in a semi-supervised learning context.
The Strategy Behind Selective Sampling
This approach focuses on querying labels for instances where the model is most uncertain, effectively using limited labeling resources. It's particularly useful in scenarios where labeling data is resource-intensive.
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