Ensemble Machine Learning: Methods and Applications eBook

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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 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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Additional ISBNs
9781489988171

Ensemble Machine Learning: Methods and Applications is written by Cha Zhang and published by Springer. The Digital and eTextbook ISBNs for Ensemble Machine Learning are 9781441993267, 1441993266 and the print ISBNs are 9781441993250, 1441993258. Additional ISBNs for this eTextbook include 9781489988171.