國立虎尾科技大學 |

Granular Computing Based Machine Learning = A Big Data Processing Approach /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Granular Computing Based Machine Learning/ by Han Liu, Mihaela Cocea.
Reminder of title:	A Big Data Processing Approach /
Author:	Liu, Han.
other author:	Cocea, Mihaela.
Description:	XV, 113 p. 27 illus., 19 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-70058-8
ISBN:	9783319700588

Granular Computing Based Machine Learning = A Big Data Processing Approach /
Liu, Han.

Granular Computing Based Machine LearningA Big Data Processing Approach /[electronic resource] :by Han Liu, Mihaela Cocea. - 1st ed. 2018. - XV, 113 p. 27 illus., 19 illus. in color.online resource. - Studies in Big Data,352197-6503 ;. - Studies in Big Data,8.

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

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

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Granular Computing Based Machine Learning = A Big Data Processing Approach /
LDR:03289nam a22003975i 4500 001 998446
003 DE-He213
005 20200701063121.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319700588 $9 978-3-319-70058-8
024 7 $a 10.1007/978-3-319-70058-8 $2 doi
035 $a 978-3-319-70058-8
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Liu, Han. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1102745
245 1 0 $a Granular Computing Based Machine Learning $h [electronic resource] : $b A Big Data Processing Approach / $c by Han Liu, Mihaela Cocea.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XV, 113 p. 27 illus., 19 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Big Data, $x 2197-6503 ; $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 Computational intelligence. $3 568984
650 0 $a Big data. $3 981821
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Big Data/Analytics. $3 1106909
700 1 $a Cocea, Mihaela. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1102747
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319700571
776 0 8 $i Printed edition: $z 9783319700595
776 0 8 $i Printed edition: $z 9783319888842
830 0 $a Studies in Big Data, $x 2197-6503 ; $v 8 $3 1256918
856 4 0 $u https://doi.org/10.1007/978-3-319-70058-8
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)