Active Learning of Halfspaces under a Margin Assumption

Details Active Learning of Halfspaces under a Margin Assumption Active Learning of Halfspaces under a Margin Assumption

2011-12-07

arxiv.org/abs/1112.1556v3

Computer Science

Learning

A more detailed exposition; Added a description of a simpler implementation and results of experiments

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Active Learning of Halfspaces under a Margin Assumption does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Active Learning of Halfspaces under a Margin Assumption, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Active Learning of Halfspaces under a Margin Assumption will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-595484