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.
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.
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
Multimodal Analytics for Next-Generation Big Data Technologies and Applications
eBook (1st ed. 2019)
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Product Details
| ISBN-13: | 9783319975986 |
|---|---|
| Publisher: | Springer-Verlag New York, LLC |
| Publication date: | 07/18/2019 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| File size: | 67 MB |
| Note: | This product may take a few minutes to download. |