Activized Learning: Transforming Passive to Active with Improved Label Complexity

Details Activized Learning: Transforming Passive to Active with Improved Label Complexity Activized Learning: Transforming Passive to Active with Improved Label Complexity

2011-08-08

arxiv.org/abs/1108.1766v1

Statistics

Machine Learning

Scientific paper

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions. We further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient. We also extend these results to active learning in the presence of label noise, and find that even under broad classes of noise distributions, we can typically guarantee strict improvements over the known results for passive learning.

Affiliated with

Also associated with

No associations

Rating

Activized Learning: Transforming Passive to Active with Improved Label Complexity 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 Activized Learning: Transforming Passive to Active with Improved Label Complexity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Activized Learning: Transforming Passive to Active with Improved Label Complexity will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-190599