大葉大學圖書館 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.312
書名/作者:	Feature selection for high-dimensional data/ by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos.
作者:	Bolon-Canedo, Veronica.
其他作者:	Sanchez-Marono, Noelia.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xv, 147 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
標題:	Database management.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Data Mining and Knowledge Discovery.
標題:	Data Structures.
ISBN:	9783319218588
ISBN:	9783319218571
內容註:	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.
摘要、提要註:	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.
電子資源:	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] /
LDR:02435nam a2200337 a 4500 001 443822
003 DE-He213
005 20160422160819.0
006 m d
007 cr nn 008maaau
008 160715s2015 gw s 0 eng d
020 $a 9783319218588 $q (electronic bk.)
020 $a 9783319218571 $q (paper)
024 7 $a 10.1007/978-3-319-21858-8 $2 doi
035 $a 978-3-319-21858-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b B693 2015
100 1 $a Bolon-Canedo, Veronica. $3 634857
245 1 0 $a Feature selection for high-dimensional data $h [electronic resource] / $c by Veronica Bolon-Canedo, Noelia Sanchez-Marono, Amparo Alonso-Betanzos.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xv, 147 p. : $b ill., digital ; $c 24 cm.
490 1 $a Artificial intelligence: foundations, theory, and algorithms, $x 2365-3051
505 0 $a 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.
520 $a 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.
650 0 $a Data mining. $3 337740
650 0 $a Database management. $3 174575
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
650 2 4 $a Data Structures. $3 467911
700 1 $a Sanchez-Marono, Noelia. $3 634858
700 1 $a Alonso-Betanzos, Amparo. $3 634859
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Artificial intelligence: foundations, theory, and algorithms. $3 633948
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21858-8
950 $a Computer Science (Springer-11645)

	個人資料
	儲存檢索策略
	薦購
	預約/借閱記錄查詢
	訊息
	評論
	個人書籤

	簡單查詢
	進階查詢

	指定參考書
	新書通報
	新書書單RSS