IT training ensemble machine learning methods and applications zhang ma 2012 02 17
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IT training ensemble machine learning methods and applications zhang ma 2012 02 17
Ensemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17oft Road 98052 RedmondUSAYunqian MaHoneywellDouglas Drive North 1985 55422 Golden ValleyUSAISBN 978-1-4419-9325-0 e-ISBN 978-1-4419-9326-7DOI 10.1007/978-1-4419-9326-7Springer New York Dordrecht Heidelberg LondonLibrary of Congress Control Number: 2012930830© Springer Scicncc+Busincss Media. LLC 201 IT training ensemble machine learning methods and applications zhang ma 2012 02 172All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+IT training ensemble machine learning methods and applications zhang ma 2012 02 17
Business Media. LLC. 233 Spring Street. New York. NY 10013. USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in cEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17own or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if they arc not identified as such, is not to be taken as an expression of opinion as to whether or not they arc subject to proprietary rights.Printed on acid-frcc p IT training ensemble machine learning methods and applications zhang ma 2012 02 17aperSpringer is part of Springer Scicncc+Busincss Media (www.springcr.com)PrefaceMaking decisions based on the input of multiple people or experts hasIT training ensemble machine learning methods and applications zhang ma 2012 02 17
been a common practice in human civilization and serves as the foundation of a democratic society. Over the past few decades, researchers in the compEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17erred 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 IT training ensemble machine learning methods and applications zhang ma 2012 02 17 and random forest. Today, ensemble learning has many real-world applications, including object detection and tracking, scene segmentation and analysiIT training ensemble machine learning methods and applications zhang ma 2012 02 17
s, image recognition, information retrieval, bioinformatics, data mining, etc. To give a concrete example, most modern digital cameras are equipped wiEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17ecting 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 IT training ensemble machine learning methods and applications zhang ma 2012 02 17 algorithm adopted in the Xbox Kinect sensor, which allows people to interact with games freely without game controllers.Despite the great success ofIT training ensemble machine learning methods and applications zhang ma 2012 02 17
ensemble learning methods recently, we found very few books that were dedicated to this topic, and even fewer that provided insights about how such meEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17er 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 contri IT training ensemble machine learning methods and applications zhang ma 2012 02 17butions. In particular, this book contains chapters contributed by researchers in both academia and leading industrial research labs. It shall serve tIT training ensemble machine learning methods and applications zhang ma 2012 02 17
he needs of different readers at different levels. For readers w ho are new to the subject, the book provides an excellent entry point withVviPrefaceaEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17is a handy reference summarizing the up-to-date advances in ensemble learning, their connections, and future directions. For practitioners, the book provides a number of applications for ensemble learning and offers examples of successful, real-world systems.This book consists of two parts. The firs IT training ensemble machine learning methods and applications zhang ma 2012 02 17t part, from Chaps. I to 7. focuses more on the theory aspect of ensemble learning. The second part, from Chaps. 8 to II. presents a few applicationsIT training ensemble machine learning methods and applications zhang ma 2012 02 17
for ensemble learning.Chapter 1, as an introduction for this book, provides an overview of various methods in ensemble learning. A review of the well-Ensemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17 of which use kernel estimators as weak learners. Chapter 4 describes a “targeted learning” scheme for the estimation of nonpathwise differentiable parameters and considers a loss-based super learner that uses the cross-validated empirical mean of the estimated loss as estimator of risk. Random fore IT training ensemble machine learning methods and applications zhang ma 2012 02 17st is discussed in detail in Chap. 5. Chapter 6 presents negative correlationbased ensemble learning for improving diversity, which introduces the negIT training ensemble machine learning methods and applications zhang ma 2012 02 17
atively correlated ensemble learning algorithm and explains that regularization is an important factor to address the overfitting problem for noisy daEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17ate low rank approximations than the standard Nystrom method. Ensemble learning applications are presented from Chaps.8 to II. Chapter 8 explains how the boosting algorithm can be applied in object detection tasks, where positive examples are rare and the detection speed is critical. Chapter 9 prese IT training ensemble machine learning methods and applications zhang ma 2012 02 17nts various ensemble learning techniques that have been applied to the problem of human activity recognition. Boosting algorithms for medical applicatIT training ensemble machine learning methods and applications zhang ma 2012 02 17
ions, especially medical image analysis arc described in Chap. 10. and random forest for bioinformatics applications is demonstrated in Chap. 11. OverEnsemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne Microso IT training ensemble machine learning methods and applications zhang ma 2012 02 17ike to sincerely thank all the contributors of this book for presenting their research in an easily accessible manner, and for putting such discussion into a historical context. We would like to thank Bren Kurzman of Springer for his strong support to this book.Redmond. WA IT training ensemble machine learning methods and applications zhang ma 2012 02 17Ensemble Machine LearningCha Zhang • Yunqian Ma EditorsEnsemble Machine LearningMethods and Applications0 SpringerEditorsCha ZhangMicrosoftOne MicrosoGọi ngay
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