大葉大學圖書館 |

Feature selection for high-dimensional data[electronic resource] /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	006.312
[NT 47271] Title/Author:	Feature selection for high-dimensional data/ by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos.
作者:	Bolon-Canedo, Veronica.
[NT 51406] other author:	Sanchez-Marono, Noelia.
出版者:	Cham : : Springer International Publishing :, 2015.
面页册数:	xv, 147 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Data mining.
标题:	Database management.
标题:	Computer Science.
标题:	Artificial Intelligence (incl. Robotics)
标题:	Data Mining and Knowledge Discovery.
标题:	Data Structures.
ISBN:	9783319218588
ISBN:	9783319218571
[NT 15000228] null:	Introduction to High-Dimensionality -- Foundations of Feature Selection -- Experimental Framework -- Critical Review of Feature Selection Methods -- Application of Feature Selection to Real Problems -- Emerging Challenges.
[NT 15000229] null:	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.
电子资源:	http://dx.doi.org/10.1007/978-3-319-21858-8

Feature selection for high-dimensional data[electronic resource] /
Bolon-Canedo, Veronica.

Feature selection for high-dimensional data[electronic resource] /by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos. - Cham :Springer International Publishing :2015. - xv, 147 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-3051. - Artificial intelligence: foundations, theory, and algorithms..

Introduction to High-Dimensionality -- Foundations of Feature Selection -- Experimental Framework -- Critical Review of Feature Selection Methods -- Application of Feature Selection to Real Problems -- Emerging Challenges.

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.

ISBN: 9783319218588

Standard No.: 10.1007/978-3-319-21858-8doiSubjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Feature selection for high-dimensional data[electronic resource] /
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