Ensemble Machine Learning

Methods and Applications

Editors: Zhang, Cha, Ma, Yunqian (Eds.)

  • 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
see more benefits

Buy this book

eBook $169.00
price for USA (gross)
  • ISBN 978-1-4419-9326-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $219.99
price for USA
  • ISBN 978-1-4419-9325-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $169.99
price for USA
  • ISBN 978-1-4899-8817-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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.

About the authors

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

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)


Table of contents (11 chapters)

  • Ensemble Learning

    Polikar, Robi

    Pages 1-34

  • Boosting Algorithms: A Review of Methods, Theory, and Applications

    Ferreira, Artur J. (et al.)

    Pages 35-85

  • Boosting Kernel Estimators

    Marzio, Marco (et al.)

    Pages 87-115

  • Targeted Learning

    Laan, Mark J. (et al.)

    Pages 117-156

  • Random Forests

    Cutler, Adele (et al.)

    Pages 157-175

Buy this book

eBook $169.00
price for USA (gross)
  • ISBN 978-1-4419-9326-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $219.99
price for USA
  • ISBN 978-1-4419-9325-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $169.99
price for USA
  • ISBN 978-1-4899-8817-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Bibliographic Information

Bibliographic Information
Book Title
Ensemble Machine Learning
Book Subtitle
Methods and Applications
Editors
  • Cha Zhang
  • Yunqian Ma
Copyright
2012
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media, LLC
eBook ISBN
978-1-4419-9326-7
DOI
10.1007/978-1-4419-9326-7
Hardcover ISBN
978-1-4419-9325-0
Softcover ISBN
978-1-4899-8817-1
Edition Number
1
Number of Pages
VIII, 332
Topics