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Machine Learning in Non-Stationary Environments
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Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe

Abstract

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning’s greatest challenges. This book focuses on a specific non-stationary ... More

Keywords: computing, machine learning, data generation mechanism, image recognition, natural language processing, speech recognition, robot control, bioinformatics, non-stationarity, covariate shift

Bibliographic Information

Print publication date: 2012 Print ISBN-13: 9780262017091
Published to MIT Press Scholarship Online: September 2013 DOI:10.7551/mitpress/9780262017091.001.0001

Authors

Affiliations are at time of print publication.

Masashi Sugiyama, author

Motoaki Kawanabe, author