Ensemble Machine Learning_ Methods and Applications [Zhang & Ma 2012-02-17].pdf

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Ensemble Machine Learning
Cha Zhang • Yunqian Ma
Editors
Ensemble Machine Learning
Methods and Applications
123
Editors
Cha Zhang
Microsoft
One Microsoft Road
98052 Redmond
USA
Yunqian Ma
Honeywell
Douglas Drive North 1985
55422 Golden Valley
USA
ISBN 978-1-4419-9325-0
e-ISBN 978-1-4419-9326-7
DOI 10.1007/978-1-4419-9326-7
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2012930830
© Springer Science+Business Media, LLC 2012
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,
NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in
connection with any form of information storage and retrieval, electronic adaptation, computer software,
or by similar or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject
to proprietary rights.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Making decisions based on the input of multiple people or experts has been a
common practice in human civilization and serves as the foundation of a democratic
society. Over the past few decades, researchers in the computational intelligence
and machine learning community have studied schemes that share such a joint
decision procedure. These schemes are generally referred to as ensemble learning,
which is known to reduce the classifiers’ variance and improve the decision system’s
robustness and accuracy.
However, it was not until recently that researchers were able to fully unleash the
power and potential of ensemble learning with new algorithms such as boosting
and random forest. Today, ensemble learning has many real-world applications,
including object detection and tracking, scene segmentation and analysis, image
recognition, information retrieval, bioinformatics, data mining, etc. To give a
concrete example, most modern digital cameras are equipped with face detection
technology. While the human neural system has evolved for millions of years to
recognize human faces efficiently and accurately, detecting faces by computers has
long been one of the most challenging problems in computer vision. The problem
was largely solved by Viola and Jones, who developed a high-performance face
detector based on boosting (more details in Chap. 8). Another example is the random
forest-based skeleton tracking algorithm adopted in the Xbox Kinect sensor, which
allows people to interact with games freely without game controllers.
Despite the great success of ensemble learning methods recently, we found very
few books that were dedicated to this topic, and even fewer that provided insights
about how such methods shall be applied in real-world applications. The primary
goal of this book is to fill the existing gap in the literature and comprehensively cover
the state-of-the-art ensemble learning methods, and provide a set of applications
that demonstrate the various usages of ensemble learning methods in the real world.
Since ensemble learning is still a research area with rapid developments, we invited
well-known experts in the field to make contributions. In particular, this book
contains chapters contributed by researchers in both academia and leading industrial
research labs. It shall serve the needs of different readers at different levels. For
readers who are new to the subject, the book provides an excellent entry point with
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