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Engineering - Computational Intelligence and Complexity | Ensemble Machine Learning - Methods and Applications

Ensemble Machine Learning

Methods and Applications

Zhang, Cha, Ma, Yunqian (Eds.)

2012, VIII, 332 p.

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  • 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

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.

Content Level » Research

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

Related subjects » Computational Intelligence and Complexity - Computer Science - Database Management & Information Retrieval

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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