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Ensemble Machine Learning

Methods and Applications

  • Book
  • © 2012

Overview

  • Covers all existing methods developed for ensemble learning
  • Presents overview and in-depth knowledge about ensemble learning
  • Discusses the pros and cons of various ensemble learning methods
  • Demonstrate how ensemble learning can be used with real world applications

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About this book

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 recognition and are now being applied in areas as diverse as object tracking and 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 the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

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Keywords

Table of contents (11 chapters)

Reviews

From the reviews:

“The book itself is written by an ensemble of experts. Each of the 11 chapters is written by one or more authors, and each approaches the subject from a different direction. … This is an excellent book for someone who has already learned the basic machine learning tools. It would work well as a textbook or resource for a second course on machine learning. The algorithms are clearly presented in pseudocode form, and each chapter has its own references (about 50 on average).” (D. L. Chester, ACM Computing Reviews, July, 2012)

Editors and Affiliations

  • Microsoft, Redmond, USA

    Cha Zhang

  • Honeywell, Golden Valley, USA

    Yunqian Ma

About the editors

Dr. Zhang works for Microsoft. Dr. Ma works for Honeywell.    

Bibliographic Information

  • Book Title: Ensemble Machine Learning

  • Book Subtitle: Methods and Applications

  • Editors: Cha Zhang, Yunqian Ma

  • DOI: https://doi.org/10.1007/978-1-4419-9326-7

  • Publisher: Springer New York, NY

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Science+Business Media, LLC 2012

  • Hardcover ISBN: 978-1-4419-9325-0Published: 17 February 2012

  • Softcover ISBN: 978-1-4899-8817-1Published: 12 April 2014

  • eBook ISBN: 978-1-4419-9326-7Published: 17 February 2012

  • Edition Number: 1

  • Number of Pages: VIII, 332

  • Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Computer Science, general

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