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

Kawanabe, Motoaki, Sugiyama, Masashi, Bach, Francis

ISBN 10: 0262017091 / ISBN 13: 9780262017091

Published by MIT Press, 2012

About this Item

Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Bibliographic Details

• Title: Machine Learning in Non-Stationary Environments : Introduction to Covariate Shift Adaptation
• Publisher: MIT Press
• Publication Date: 2012
• Language: English
• ISBN 10: 0262017091
• ISBN 13: 9780262017091
• Binding: Hardcover
• Condition: Very Good
• Weight: 1.071 pounds

About this title

Synopsis

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity.

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 environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.

After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

About the Author

Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology. Motoaki Kawanabe is a Postdoctoral Researcher in Intelligent Data Analysis at the Fraunhofer FIRST Institute, Berlin. In October 2011, he moved to Advanced Telecommunications Research Institute International (ATR) in Kyoto, Japan.