Active Learning
Active learning is a machine learning paradigm where an algorithm interactively queries a user or another information source to label new data points. Instead of passively receiving a large, pre-labeled dataset, the active learner strategically chooses which unlabeled examples would be most informative to label, aiming to achieve high accuracy with fewer labeled examples. This is particularly useful in scenarios where data labeling is expensive or time-consuming, like medical image annotation or rare event detection. Query strategies often include uncertainty sampling or diversity sampling.
It's like a curious student who asks specific questions about topics they don't understand to learn more efficiently, rather than just passively listening to every lecture.
Active learning can significantly reduce the cost and effort of data labeling, making it feasible to build effective AI models in domains with limited or expensive labeled data.
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