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Machine Learning in Non-Stationary EnvironmentsIntroduction to Covariate Shift Adaptation$
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Masashi Sugiyama and Motoaki Kawanabe

Print publication date: 2012

Print ISBN-13: 9780262017091

Published to MIT Press Scholarship Online: September 2013

DOI: 10.7551/mitpress/9780262017091.001.0001

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Active Learning

Active Learning

(p.182) (p.183) 8 Active Learning
Machine Learning in Non-Stationary Environments

Masashi Sugiyama

Motoaki Kawanabe

The MIT Press

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.

Keywords:   active learning, regression, covariate shift, pool-based active learning

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