Steve Hanneke, Varun Kanade, Liu Yang
Published 2015-05-20Version 1
We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.
Comparison three methods of clustering: k-means, spectral clustering and hierarchical clustering
Active and passive learning of linear separators under log-concave distributions
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
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