This chapter examines the problem of active learning. The goal of active learning is to find the most “informative” training input points so that learning can be successfully achieved from only a small number of training samples. In the active learning scenario, covariate shift—mismatch of training and test input distributions—occurs naturally occurs since the training input distribution is designed by users, while the test input distribution is determined by the environment. Thus, covariate shift is inevitable in active learning. The chapter introduces active learning methods for regression in light of covariate shift. Their mutual relation and numerical examples are also shown. Furthermore, these active learning methods are extended to the pool-based scenarios, where a set of input-only samples is provided in advance and users want to specify good input-only samples to gather output values.
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.