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Sinopsis
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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Dr. Zhang works for Microsoft. Dr. Ma works for Honeywell.
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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 detection and are now being applied in areas as diverse as object trackingand 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 various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
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Ensemble Machine Learning
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Springer New York Apr 2014, 2014
ISBN 10: 1489988173
ISBN 13: 9781489988171
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 340 pp. Englisch. Nº de ref. del artículo: 9781489988171
Cantidad disponible: 2 disponibles
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Ensemble Machine Learning
Impresión bajo demandaLibrería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers all existing methods developed for ensemble learningPresents overview and in-depth knowledge about ensemble learningDiscusses the pros and cons of various ensemble learning methodsDemonstrate how ensemble learning can be used . Nº de ref. del artículo: 4212968
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Ensemble Machine Learning (eng)
Impresión bajo demandaLibrería: Brook Bookstore On Demand, Napoli, NA, Italia
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Condición: new. Questo è un articolo print on demand. Nº de ref. del artículo: 7055025ba0351a03b9faaecb362334ed
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Ensemble Machine Learning: Methods and Applications
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Ensemble Machine Learning : Methods and Applications
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Springer New York, Springer US, 2014
ISBN 10: 1489988173
ISBN 13: 9781489988171
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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: 9781489988171
Cantidad disponible: 1 disponibles
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Ensemble Machine Learning
Publicado por
Springer New York, Springer US Apr 2014, 2014
ISBN 10: 1489988173
ISBN 13: 9781489988171
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch. Nº de ref. del artículo: 9781489988171
Cantidad disponible: 1 disponibles
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Ensemble Machine Learning
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. 340. Nº de ref. del artículo: 26142274310
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Ensemble Machine Learning: Methods and Applications
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Springer-Verlag New York Inc, 2014
ISBN 10: 1489988173
ISBN 13: 9781489988171
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Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. 340 pages. 9.25x6.10x0.77 inches. In Stock. Nº de ref. del artículo: x-1489988173
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Ensemble Machine Learning
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 340 84 Illus. Nº de ref. del artículo: 135090393
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Ensemble Machine Learning
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 340. Nº de ref. del artículo: 18142274316
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