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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 with Model Selection

Active Learning with Model Selection

Chapter:
(p.215) 9 Active Learning with Model Selection
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0009

This chapter examines the problem of active learning with model selection. Model selection and active learning are two important challenges for successful learning. A natural desire is to perform model selection and active learning at the same time, that is, we want to choose the best model and the best training input points. However, this is actually a chicken-and-egg problem since training input samples should have been fixed for performing model selection and models should have been fixed for performing active learning. The chapter discusses several compromise approaches, such as the sequential approach, the batch approach, and the ensemble approach. Using numerical examples, limitations of the sequential and batch approaches are pointed out, and the usefulness of the ensemble active learning approach is demonstrated.

Keywords:   active learning, model selection, sequential approach, batch approach, ensemble approach

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