Ensemble Machine Learning: Methods and Applications

Author:   Cha Zhang ,  Yunqian Ma
Publisher:   Springer-Verlag New York Inc.
Edition:   2012
ISBN:  

9781441993250


Pages:   332
Publication Date:   17 February 2012
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Ensemble Machine Learning: Methods and Applications


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Overview

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.

Full Product Details

Author:   Cha Zhang ,  Yunqian Ma
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2012
Dimensions:   Width: 15.50cm , Height: 2.00cm , Length: 23.50cm
Weight:   0.676kg
ISBN:  

9781441993250


ISBN 10:   1441993258
Pages:   332
Publication Date:   17 February 2012
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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.

Reviews

From the reviews: The book itself is written by an ensemble of experts. Each of the 11 chapters is written by one or more authors, and each approaches the subject from a different direction. ... This is an excellent book for someone who has already learned the basic machine learning tools. It would work well as a textbook or resource for a second course on machine learning. The algorithms are clearly presented in pseudocode form, and each chapter has its own references (about 50 on average). (D. L. Chester, ACM Computing Reviews, July, 2012)


Author Information

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

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