Hardcover | $49.00 Short | £33.95 | ISBN: 9780262017091 | 280 pp. | 6 x 9 in | 78 b&w illus.| March 2012 Ebook |$35.00 Short | ISBN: 9780262301978 | 280 pp. | 6 x 9 in | 78 b&w illus.| March 2012

# Machine Learning in Non-Stationary Environments

## Overview

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.

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.

## Endorsements

“Though important in practice and conceptually intriguing, the topic of covariate shift adaptation has only recently begun to attract significant attention in machine learning. Building on their sample reweighting methods, the authors assay a core problem of robust empirical inference. This timely book should be recommended to researchers and practitioners in a range of disciplines.”
Bernhard Schölkopf, Max Planck Institute for Intelligent Systems

“In machine learning we often assume that the characteristics of the data used to design a system will remain the same once the system is deployed. When this assumption is violated, and it does happen often, a system's accuracy may suffer significantly. This book provides the first in-depth look at how one can prepare for and cope with a frequently occurring instance of the above problem (covariate shift) both from theoretical and practical perspectives.”
Neil Rubens, University of Electro-Communications, Japan

“Written by two active researchers in the area, this book provides a highly accessible and self-contained exposition to some of the most important and recent advancements for tackling the covariate-shift problem. Students, researchers, and practitioners in related fields will benefit greatly from its huge collection of algorithms, numerical examples, and real-life applications.”
Lihong Li, Yahoo! Research

“This book provides a clear and practical guide to the problem of learning when the training and test data are drawn from different distributions. Of particular value are the many worked examples, illustrating the operation of the described techniques on real-life problems, and demonstrating their strengths, limitations, and areas of application.”
Arthur Gretton, Gatsby Computational Neuroscience Unit, CSML, University College London