Ensemble Machine Learning (Hardcover)

Cha Zhang

ISBN 10: 1441993258 / ISBN 13: 9781441993250

Published by Springer-Verlag New York Inc., New York, NY, 2012

About this Item

Hardcover. 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 systems 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781441993250

Bibliographic Details

• Title: Ensemble Machine Learning (Hardcover)
• Publisher: Springer-Verlag New York Inc., New York, NY
• Publication Date: 2012
• Language: English
• ISBN 10: 1441993258
• ISBN 13: 9781441993250
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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 Author

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