Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptationby Masashi Sugiyama, Motoaki Kawanabe
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
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.
What People are saying about this
"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
Meet 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.
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