Understanding Uncertainty Sampling via Equivalent Loss
The equivalent loss is introduced, whose gradient is the original loss gradient multiplied by the query probability, which derives a finite-sample excess-risk bound with a fixed learning rate and an explicit constant controlling the tradeoff between initial error and query-weighted gradient variance.