Uncertainty sampling optimizes an equivalent loss, enabling sample complexity analysis and asymptotic superiority guarantees over passive learning in binary classification.
arXiv preprint arXiv:2212.00772
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Understanding Uncertainty Sampling via Equivalent Loss
Uncertainty sampling optimizes an equivalent loss, enabling sample complexity analysis and asymptotic superiority guarantees over passive learning in binary classification.