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Ensemble Machine Learning Methods and Applications /

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 a...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Zhang, Cha. (Editor, http://id.loc.gov/vocabulary/relators/edt), Ma, Yunqian. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Format: Electronic eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2012.
Edition:1st ed. 2012.
Subjects:
Computational intelligence.
Data mining.
Computer science.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Computer Science, general.
Online Access:https://doi.org/10.1007/978-1-4419-9326-7
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Table of Contents:
  • Introduction of Ensemble Learning
  • Boosting Algorithms: Theory, Methods and Applications
  • On Boosting Nonparametric Learners
  • Super Learning
  • Random Forest
  • Ensemble Learning by Negative Correlation Learning
  • Ensemble Nystrom Method
  • Object Detection
  • Ensemble Learning for Activity Recognition
  • Ensemble Learning in Medical Applications
  • Random Forest for Bioinformatics.

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