_active-learning-drifting-distributions-and-convex-surrogate-losses-liu-yang.jpg "Active Learning, Drifting Distributions, and Convex Surrogate Losses - Liu Yang from minimax method Video Screenshot Preview")
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⏲ Duration: 22 min 85 sec
✓ Published: 15-May-2012
✓ Published: 15-May-2012
Description:
We study the problem of active learning in a stream-based setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of prediction mistakes and number of label requests for established disagreement-based active learning algorithms, both in the realizable case and under Tsybakov noise. We further prove minimax lower bounds for this problem.nnIt is often computationally hard to run these methods with the 0-1 loss. Passive learning often resolves thi
We study the problem of active learning in a stream-based setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of prediction mistakes and number of label requests for established disagreement-based active learning algorithms, both in the realizable case and under Tsybakov noise. We further prove minimax lower bounds for this problem.nnIt is often computationally hard to run these methods with the 0-1 loss. Passive learning often resolves thi
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