Ensemble Machine Learning

The primary goal of this book is to give readers a complete treatment of the state-of-the-art ensemble learning methods. It also provides a set of applications that demonstrate the various usages of ensemble learning methods in the real-world.

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.

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

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

Inhalt
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.