Ensemble methodology imitates our second nature to seek several opinions before making a crucial decision. The core principle is to weigh several individual pattern classifiers, and combine them in order to reach a classification that is better than the one obtained by each of them separately. Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methods since the late seventies. Given the growing interest in the field, it is not surprising that researchers and practitioners have a wide variety of methods at their disposal. Pattern Classification Using Ensemble Methods aims to provide a methodic and well structured introduction into this world by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. Its informative, factual pages will provide researchers, students and practitioners in industry with a comprehensive, yet concise and convenient reference source to ensemble methods. The book describes in detail the classical methods, as well as extensions and novel approaches that were recently introduced. Along with algorithmic descriptions of each method, the reader is provided with a description of the settings in which this method is applicable and with the consequences and the trade-offs incurred by using the method. This book is dedicated entirely to the field of ensemble methods and covers all aspects of this important and fascinating methodology.
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- zt_7062013-12-28蛮好的。谢谢楼主!大家也可以下来看卡
- ironman102017-11-18现有方法中,集成分类是比较有效的一种,值得借鉴
- guoxze2014-07-19不错的介绍模式分类的书,可以读一下
- denver_enter2016-05-12相当全面的集成分类方法介绍,内容很多,但是逻辑组织比较一般。
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