Ensemble Machine Learning: Methods and Applications

Zhang, Cha, Ma, Yunqian, editors

ISBN 10: 1441993258 / ISBN 13: 9781441993250

Published by Springer, 2017

About this Item

2017 printing; 340 pp., hardcover, previous owner's name neatly inked to the title page, head of spine rubbed, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Seller Inventory # ZB1316680

Bibliographic Details

• Title: Ensemble Machine Learning: Methods and Applications
• Publisher: Springer
• Publication Date: 2017
• Language: English
• ISBN 10: 1441993258
• ISBN 13: 9781441993250
• Binding: Hardcover
• Condition: Fine

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.