國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Granular computing based machine lea...

Cocea, Mihaela.

Granular computing based machine learninga big data processing approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Granular computing based machine learningby Han Liu, Mihaela Cocea.
其他題名:	a big data processing approach /
作者:	Liu, Han.
其他作者:	Cocea, Mihaela.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xv, 113 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	http://dx.doi.org/10.1007/978-3-319-70058-8
ISBN:	9783319700588$q(electronic bk.)

Granular computing based machine learninga big data processing approach /
Liu, Han.

Granular computing based machine learninga big data processing approach /[electronic resource] :by Han Liu, Mihaela Cocea. - Cham :Springer International Publishing :2018. - xv, 113 p. :ill., digital ;24 cm. - Studies in big data,v.352197-6503 ;. - Studies in big data ;v.1..

This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs--Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.

ISBN: 9783319700588$q(electronic bk.)

Standard No.: 10.1007/978-3-319-70058-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Granular computing based machine learninga big data processing approach /
LDR:02876nmm a2200313 a 4500 001 529348
003 DE-He213
005 20180719173006.0
006 m d
007 cr nn 008maaau
008 181105s2018 gw s 0 eng d
020 $a 9783319700588$q(electronic bk.)
020 $a 9783319700571$q(paper)
024 7 $a 10.1007/978-3-319-70058-8 $2 doi
035 $a 978-3-319-70058-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .L783 2018
100 1 $a Liu, Han. $3 737768
245 1 0 $a Granular computing based machine learning $h [electronic resource] : $b a big data processing approach / $c by Han Liu, Mihaela Cocea.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xv, 113 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.35
520 $a This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs--Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
650 0 $a Machine learning. $3 188639
650 0 $a Granular computing. $3 224958
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Big Data. $3 760530
650 2 4 $a Big Data/Analytics. $3 742047
700 1 $a Cocea, Mihaela. $3 737769
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.1. $3 675357
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-70058-8
950 $a Engineering (Springer-11647)