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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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Introduction and Problem Formulation

Introduction and Problem Formulation

Chapter:
(p.2) (p.3) 1 Introduction and Problem Formulation
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0001

This chapter provides an introduction to covariate shift adaptation toward machine learning in a non-stationary environment. It begins by discussing cover machine learning under covariate shift. It then describes the core idea of covariate shift adaptation, using an illustrative example. Next, it formulates the supervised learning problem, which includes regression and classification. It pays particular attention to covariate shift and model misspecification. An overview of the subsequent chapters is also presented.

Keywords:   machine learning, covariate shift adaptation, supervised learning problem, model misspecification

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