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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multimodal Analytics for Next-Generation Big Data Technologies and Applications/ edited by Kah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao.
other author:	Seng, Kah Phooi.
Description:	XV, 391 p. 150 illus., 109 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	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
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