| 书目名称 | Ensemble Machine Learning | | 副标题 | Methods and Applicat | | 编辑 | Cha Zhang,Yunqian Ma | | 视频video | | | 概述 | Covers all existing methods developed for ensemble learning.Presents overview and in-depth knowledge about ensemble learning.Discusses the pros and cons of various ensemble learning methods.Demonstrat | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2012 | | 关键词 | Bagging Predictors; Basic Boosting; Ensemble learning; Object Detection; classification algorithm; deep n | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4419-9326-7 | | isbn_softcover | 978-1-4899-8817-1 | | isbn_ebook | 978-1-4419-9326-7 | | copyright | Springer Science+Business Media, LLC 2012 |
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发表于 2025-3-21 17:40:36
