Ensemble Machine Learning: Methods and Applications

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9781441993250: Ensemble Machine Learning: Methods and 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 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.

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About the Author:

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

Review:

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)

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Editorial: Springer-Verlag New York Inc., United States (2012)
ISBN 10: 1441993258 ISBN 13: 9781441993250
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Descripción Springer-Verlag New York Inc., United States, 2012. Hardback. Estado de conservación: New. 2012. Language: English . Brand New Book. 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. Nº de ref. de la librería LIB9781441993250

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Zhang, Cha
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Cha Zhang
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Descripción Springer-Verlag New York Inc., 2012. HRD. Estado de conservación: New. New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. de la librería IP-9781441993250

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Editorial: Springer-Verlag New York Inc., United States (2012)
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Descripción Springer-Verlag New York Inc., United States, 2012. Hardback. Estado de conservación: New. 2012. Language: English . Brand New Book. 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. Nº de ref. de la librería LIB9781441993250

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Cha Zhang
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Descripción Springer-Verlag New York Inc., 2012. HRD. Estado de conservación: New. New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000. Nº de ref. de la librería IP-9781441993250

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CHA ZHANG
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Descripción Springer, 2012. Hardback. Estado de conservación: NEW. 9781441993250 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal. Nº de ref. de la librería HTANDREE0298616

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Cha Zhang (editor), Yunqian Ma (editor)
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ISBN 10: 1441993258 ISBN 13: 9781441993250
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Descripción Springer New York 2012-02-17, New York, N.Y., 2012. hardback. Estado de conservación: New. Nº de ref. de la librería 9781441993250

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Zhang, Cha [Editor]; Ma, Yunqian [Editor];
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Descripción Springer, 2012. Hardcover. Estado de conservación: New. Nº de ref. de la librería INGM9781441993250

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Descripción Springer, 2012. Estado de conservación: New. Nº de ref. de la librería L9781441993250

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Cha Zhang
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Descripción Springer-Verlag Gmbh Feb 2012, 2012. Buch. Estado de conservación: Neu. Neuware - 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. 329 pp. Englisch. Nº de ref. de la librería 9781441993250

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