Uncertainty sampling optimizes an equivalent loss, enabling sample complexity analysis and asymptotic superiority guarantees over passive learning in binary classification.
Learning Theory: 18th Annual Conference on Learning Theory, COL T 2005, Bertinoro, Italy, June 27-30
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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.