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) are two important components of supervised learning and have been studied extensively. 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. Keywords active learning, model selection, generalization error, linear regression, covariate shift, importance sampling, batch learning, sequential learning Nomenclature f(x) : learning target function D : doma...

• Type: Journal
• Year: 2008
• Where: NN
• Authors: Masashi Sugiyama, Neil Rubens
• Keywords: Active Learning, Model, Neural Networks, NN 2008, Training Input Point