Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in linear regression scenarios. Traditional active learning methods using least-squares learning often assume that the model used for learning is correctly specified. In many practical situations, however, this assumption may not be fulfilled. Recently, active learning methods using "importance"-weighted least-squares learning have been proposed, which are shown to be robust against misspecification of models. In this paper, we propose a new active learning method also using the weighted least-squares learning, which we call ALICE (Active Learning using the Importance-weighted least-squares learning based on Conditional Expectation of the generalization error). An important difference from existing methods is that we predict the conditional expectation of the generalization error given training input point...

• Type: Journal
• Year: 2006
• Where: JMLR
• Authors: Masashi Sugiyama
• Keywords: Active Learning, Active Learning Method, Generalization Error, JMLR 2006