Ensemble Machine Learning: Methods and Applications

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9781441993250: Ensemble Machine Learning: Methods and Applications
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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)

Reseña del editor:

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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Otras ediciones populares con el mismo título

9781489988171: Ensemble Machine Learning: Methods and Applications

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ISBN 10:  1489988173 ISBN 13:  9781489988171
Editorial: Springer, 2014
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Publicado por 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. Condición: 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. del artículo: LIB9781441993250

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Zhang, Cha
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Descripción Springer, 2016. Paperback. Condición: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Nº de ref. del artículo: ria9781441993250_lsuk

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Publicado por 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. Condición: 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. del artículo: LIB9781441993250

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Cha Zhang
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Descripción Springer-Verlag New York Inc., 2012. HRD. Condición: 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. del artículo: IQ-9781441993250

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Cha Zhang (editor), Yunqian Ma (editor)
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Descripción Springer New York 2012-02-17, New York, N.Y., 2012. hardback. Condición: New. Nº de ref. del artículo: 9781441993250

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Publicado por Springer (2012)
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Descripción Springer, 2012. Condición: New. Nº de ref. del artículo: L9781441993250

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Cha Zhang
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Descripción Springer-Verlag Gmbh Feb 2012, 2012. Buch. Condición: 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. del artículo: 9781441993250

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Cha Zhang
Publicado por Springer-Verlag Gmbh Feb 2012 (2012)
ISBN 10: 1441993258 ISBN 13: 9781441993250
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Descripción Springer-Verlag Gmbh Feb 2012, 2012. Buch. Condición: 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. del artículo: 9781441993250

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Cha Zhang
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Descripción Springer-Verlag Gmbh Feb 2012, 2012. Buch. Condición: 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. del artículo: 9781441993250

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Publicado por Springer 2012-02-17 (2012)
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Descripción Springer 2012-02-17, 2012. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 4-5 working days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. del artículo: NU-LBR-01039291

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