English
集成方法进行模式分类（Pattern Classification Using Ensemble Methods）
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2007k
资源说明：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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