Ensemble Machine Learning: Methods and Applications
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.
*An electronic version of a printed book that can be read on a computer or handheld device designed specifically for this purpose.
Formats for this Ebook
|Required Software||Any PDF Reader, Apple Preview|
|Supported Devices||Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch.|
|# of Devices||Unlimited|
|Flowing Text / Pages||Pages|
|The message text*:|
Biologia Centrali-Americana (Volume 19): [or, Contributions to the Knowledge of the Fauna and Flora of Mexico and Central America]
Mission-based Marketing: An Organizational Development Workbook (Wiley Nonprofit Law, Finance and Management Series)