Ensemble Machine Learning : Methods and Applications

Yunqian Ma

ISBN 10: 1441993258 / ISBN 13: 9781441993250

Verlag: Springer New York, Springer US, 2012

Beschreibung

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.

Bestandsnummer des Verkäufers 9781441993250

Bibliografische Details

• Titel: Ensemble Machine Learning : Methods and Applications
• Verlag: Springer New York, Springer US
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 1441993258
• ISBN-13: 9781441993250
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 23 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 23 mm
• Gewicht: 676 Gramm

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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