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Titlebook: Feature Selection for High-Dimensional Data; Verónica Bolón-Canedo,Noelia Sánchez-Maro?o,Amparo Book 2015 Springer International Publishin

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書(shū)目名稱Feature Selection for High-Dimensional Data
編輯Verónica Bolón-Canedo,Noelia Sánchez-Maro?o,Amparo
視頻videohttp://file.papertrans.cn/342/341566/341566.mp4
概述Explains how to choose an optimal subset of features according to a certain criterion.Coherent, comprehensive approach to feature subset selection in the scope of classification problems.Authors expla
叢書(shū)名稱Artificial Intelligence: Foundations, Theory, and Algorithms
圖書(shū)封面Titlebook: Feature Selection for High-Dimensional Data;  Verónica Bolón-Canedo,Noelia Sánchez-Maro?o,Amparo Book 2015 Springer International Publishin
描述.This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data..The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. .They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers..The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining..
出版日期Book 2015
關(guān)鍵詞Big Data; Big Dimensionality; Data Preprocessing; Data Reduction; Dimensionality Reduction; Feature Selec
版次1
doihttps://doi.org/10.1007/978-3-319-21858-8
isbn_softcover978-3-319-36643-2
isbn_ebook978-3-319-21858-8Series ISSN 2365-3051 Series E-ISSN 2365-306X
issn_series 2365-3051
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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