Active Learning, Drifting Distributions, and Convex Surrogate Losses - Liu Yang from minimax method Watch Video
Preview(s):
Gallery
Play Video: (Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)
⏲ Duration: 22 min 85 sec ✓ 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
Play Video: (Note: The default playback of the video is HD VERSION. If your browser is buffering the video slowly, please play the REGULAR MP4 VERSION or Open The Video below for better experience. Thank you!)