Active Learning of Halfspaces under a Margin Assumption
Abstract
We derive and analyze a new, efficient, pool-based active learning algorithm for halfspaces, called ALuMA. Most previous algorithms show exponential improvement in the label complexity assuming that the distribution over the instance space is close to uniform. This assumption rarely holds in practical applications. Instead, we study the label complexity under a large-margin assumption -- a much more realistic condition, as evident by the success of margin-based algorithms such as SVM. Our algorithm is computationally efficient and comes with formal guarantees on its label complexity. It also naturally extends to the non-separable case and to non-linear kernels. Experiments illustrate the clear advantage of ALuMA over other active learning algorithms.
Cite
@article{arxiv.1112.1556,
title = {Active Learning of Halfspaces under a Margin Assumption},
author = {Alon Gonen and Sivan Sabato and Shai Shalev-Shwartz},
journal= {arXiv preprint arXiv:1112.1556},
year = {2015}
}
Comments
A more detailed exposition; Added a description of a simpler implementation and results of experiments