A batch ensemble approach to active learning with model selection.
Optimally designing the location of training input points (active learning) and choosing the best model (model selection)-which have been extensively studied-are two important components of supervised learning. However, these two issues seem to have been investigated separately as two independent problems. If training input points and models are simultaneously optimized, the generalization performance would be further improved. In this paper, we propose a new approach called ensemble active learning for solving the problems of active learning and model selection at the same time. We demonstrate by numerical experiments that the proposed method compares favorably with alternative approaches such as iteratively performing active learning and model selection in a sequential manner.
Sugiyama
Masashi
M
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. sugi@cs.titech.ac.jp
Rubens
Neil
N
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016014:Linear Models; D008959:Models, Neurological; D015233:Models, Statistical