Defensive Universal Learning with Experts

Details Defensive Universal Learning with Experts Defensive Universal Learning with Experts

2005-07-18

arxiv.org/abs/cs/0507044v1

Proc. 16th International Conf. on Algorithmic Learning Theory (ALT 2005) 356-370

Computer Science

Learning

15 LaTeX pages

Scientific paper

This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with countably infinite expert classes, and (c) it copes with losses that may grow in time appropriately slowly. We prove loss bounds against an adaptive adversary. From this, we obtain a master algorithm for "reactive" experts problems, which means that the master's actions may influence the behavior of the adversary. Our algorithm can significantly outperform standard experts algorithms on such problems. Finally, we combine it with a universal expert class. The resulting universal learner performs -- in a certain sense -- almost as well as any computable strategy, for any online decision problem. We also specify the (worst-case) convergence speed, which is very slow.

Affiliated with

Also associated with

No associations

Rating

Defensive Universal Learning with Experts 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 Defensive Universal Learning with Experts, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Defensive Universal Learning with Experts will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-187963