הבנת סימון הביטחון דרך תבונה שקולה

תקציר מקורי באנגליתarXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduce the equivalent loss, whose gradient is the original loss gradient multiplied by the query probability. This construction places probabilistic, margin-based, and threshold-based uncertainty rules within a common framework. For binary classification, concrete equivalent losses and surrogate link functions show how uncertainty weighting preserves calibration while reshaping optimization geometry. When the equivalent loss is convex, we derive a finite-sample excess-risk bound with a fixed learning rate and an explicit constant controlling the tradeoff between initial error and query-weighted gra