Ensemble Machine Learning Methods and Applications /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Zhang, Cha (Editor), Ma, Yunqian (Editor)
• Format: Electronic eBook
• Language: English
• Published: Boston, MA : Springer US, 2012.
• Subjects: Engineering.; Computer science.; Data mining.; Computational intelligence.; Computational Intelligence.; Data Mining and Knowledge Discovery.; Computer Science, general.

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...

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.