Ensemble Machine Learning

Methods and Applications

  • Cha Zhang
  • Yunqian Ma

Table of contents

  1. Front Matter
    Pages i-viii
  2. Robi Polikar
    Pages 1-34
  3. Artur J. Ferreira, Mário A. T. Figueiredo
    Pages 35-85
  4. Marco Di Marzio, Charles C. Taylor
    Pages 87-115
  5. Mark J. van der Laan, Maya L. Petersen
    Pages 117-156
  6. Adele Cutler, D. Richard Cutler, John R. Stevens
    Pages 157-175
  7. Huanhuan Chen, Anthony G. Cohn, Xin Yao
    Pages 177-201
  8. Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
    Pages 203-223
  9. Jianxin Wu, James M. Rehg
    Pages 225-250
  10. S. Kevin Zhou, Jingdan Zhang, Yefeng Zheng
    Pages 273-306
  11. Yanjun Qi
    Pages 307-323
  12. Back Matter
    Pages 325-329

About this book

Introduction

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.

 

Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Keywords

Bagging Predictors Basic Boosting Ensemble learning Object Detection classification algorithm deep neural networks machine learning random forest stacked generalization statistical classifiers

Editors and affiliations

  • Cha Zhang
    • 1
  • Yunqian Ma
    • 2
  1. 1.MicrosoftRedmondUSA
  2. 2.HoneywellGolden ValleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-9326-7
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, Boston, MA
  • eBook Packages Engineering
  • Print ISBN 978-1-4419-9325-0
  • Online ISBN 978-1-4419-9326-7
  • About this book