PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
Philippe Rolet, Michele Sebag and Olivier Teytaud
ECML PKDD '09: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases pp. 302-317, 2009.

Abstract

This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limited to a few dozen or hundred examples due to computational constraints. Active Learning under bounded resources is formalized as a finite horizon Reinforcement Learning problem, where the sampling strategy aims at minimizing the expectation of the generalization error. A tractable approximation of the optimal (intractable) policy is presented, the Bandit-based Active Learner (BAAL) algorithm. Viewing Active Learning as a single-player game, BAAL combines UCT, the tree structured multi-armed bandit algorithm proposed by Kocsis and Szepesv´ari (2006), and billiard algorithms. A proof of principle of the approach demonstrates its good empirical convergence toward an optimal policy and its ability to incorporate prior AL criteria. Its hybridization with the Query-by-Committee approach is found to improve on both stand-alone BAAL and stand-alone QbC.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5877
Deposited By:Philippe Rolet
Deposited On:08 March 2010