國立虎尾科技大學 |

Multimodal Analytics for Next-Generation Big Data Technologies and Applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multimodal Analytics for Next-Generation Big Data Technologies and Applications/ edited by Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao.
其他作者:	Seng, Kah Phooi.
面頁冊數:	XV, 391 p. 150 illus., 109 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-97598-6
ISBN:	9783319975986

Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Multimodal Analytics for Next-Generation Big Data Technologies and Applications[electronic resource] /edited by Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao. - 1st ed. 2019. - XV, 391 p. 150 illus., 109 illus. in color.online resource.

Foundations and Principles -- Advanced Information and Knowledge Processing -- Advanced Models and Architectures -- Advanced Applications and Future Trends.

This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.

ISBN: 9783319975986

Standard No.: 10.1007/978-3-319-97598-6doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Multimodal Analytics for Next-Generation Big Data Technologies and Applications
LDR:02227nam a22003855i 4500 001 1014363
003 DE-He213
005 20200706223837.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319975986 $9 978-3-319-97598-6
024 7 $a 10.1007/978-3-319-97598-6 $2 doi
035 $a 978-3-319-97598-6
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Multimodal Analytics for Next-Generation Big Data Technologies and Applications $h [electronic resource] / $c edited by Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XV, 391 p. 150 illus., 109 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
505 0 $a Foundations and Principles -- Advanced Information and Knowledge Processing -- Advanced Models and Architectures -- Advanced Applications and Future Trends.
520 $a This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Artificial Intelligence. $3 646849
700 1 $a Seng, Kah Phooi. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1229154
700 1 $a Ang, Li-minn. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1079108
700 1 $a Liew, Alan Wee-Chung. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1308569
700 1 $a Gao, Junbin. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1308570
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319975979
776 0 8 $i Printed edition: $z 9783319975993
856 4 0 $u https://doi.org/10.1007/978-3-319-97598-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)