Multimedia Data Mining and Analytics (Disruptive Innovation)
This book reflects on the major focus shifts in multimedia data mining research and applications toward networked social communities, mobile devices, and sensors. Vast amount of multimedia are produced, shared, and accessed everyday in various social platforms. These multimedia objects (images, vi...
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2015
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oai:scholar.dlu.edu.vn:DLU123456789-562372023-11-11T05:32:47Z Multimedia Data Mining and Analytics (Disruptive Innovation) Baughman, Aaron K. Gao, Jiang Pan, Jia-Yu Petrushin, Valery A. Data Multimedia This book reflects on the major focus shifts in multimedia data mining research and applications toward networked social communities, mobile devices, and sensors. Vast amount of multimedia are produced, shared, and accessed everyday in various social platforms. These multimedia objects (images, videos, texts, tags, sensor readings, etc.) represent rich, multifaceted recordings of human behavior in the networked society, which lead to a range of important social applications, such as consumer behavior forecasting for business to optimize advertising and product recommendations, local knowledge discovery to enrich customer experience (e.g., for tourism or shopping), detection of emergent news events and trends, etc. In addition to techniques for mining single media items, all these applications require new methods for discovering robust features and stable relationships among the content of different media modalities and users, in a dynamic, social context rich, and likely noisy environment.... 2015-06-12T02:37:06Z 2015-06-12T02:37:06Z 2015 Book 978-3-319-14997-4 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/56237 en application/pdf Springer |
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Data Multimedia |
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Data Multimedia Baughman, Aaron K. Gao, Jiang Pan, Jia-Yu Petrushin, Valery A. Multimedia Data Mining and Analytics (Disruptive Innovation) |
description |
This book reflects on the major focus shifts in multimedia data mining research
and applications toward networked social communities, mobile devices, and sensors. Vast amount of multimedia are produced, shared, and accessed everyday in
various social platforms. These multimedia objects (images, videos, texts, tags,
sensor readings, etc.) represent rich, multifaceted recordings of human behavior in
the networked society, which lead to a range of important social applications, such
as consumer behavior forecasting for business to optimize advertising and product
recommendations, local knowledge discovery to enrich customer experience (e.g.,
for tourism or shopping), detection of emergent news events and trends, etc. In
addition to techniques for mining single media items, all these applications require
new methods for discovering robust features and stable relationships among the
content of different media modalities and users, in a dynamic, social context rich,
and likely noisy environment.... |
format |
Book |
author |
Baughman, Aaron K. Gao, Jiang Pan, Jia-Yu Petrushin, Valery A. |
author_facet |
Baughman, Aaron K. Gao, Jiang Pan, Jia-Yu Petrushin, Valery A. |
author_sort |
Baughman, Aaron K. |
title |
Multimedia Data Mining
and Analytics
(Disruptive Innovation) |
title_short |
Multimedia Data Mining
and Analytics
(Disruptive Innovation) |
title_full |
Multimedia Data Mining
and Analytics
(Disruptive Innovation) |
title_fullStr |
Multimedia Data Mining
and Analytics
(Disruptive Innovation) |
title_full_unstemmed |
Multimedia Data Mining
and Analytics
(Disruptive Innovation) |
title_sort |
multimedia data mining
and analytics
(disruptive innovation) |
publisher |
Springer |
publishDate |
2015 |
url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/56237 |
_version_ |
1782535502334787584 |