Ensemble Machine Learning

Yunqian Ma

ISBN 10: 1441993258 / ISBN 13: 9781441993250

Editore: Springer New York, Springer US Feb 2012, 2012

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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.

Codice articolo 9781441993250

Dati bibliografici

• Titolo: Ensemble Machine Learning
• Editore: Springer New York, Springer US Feb 2012
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1441993258
• ISBN 13: 9781441993250
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 23 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 23 millimetri di profondità
• Peso: 676 grammi

Descrizione articolo

Riassunto

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 systems 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.

Informazioni sull?autore

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