Bayesian Active Learning for Classification and Preference Learning

Details Bayesian Active Learning for Classification and Preference Learning Bayesian Active Learning for Classification and Preference Learning

2011-12-24

arxiv.org/abs/1112.5745v1

Statistics

Machine Learning

Scientific paper

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

Affiliated with

Also associated with

No associations

Rating

Bayesian Active Learning for Classification and Preference Learning 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 Bayesian Active Learning for Classification and Preference Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bayesian Active Learning for Classification and Preference Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-591300