000 | 03689nam a22005415i 4500 | ||
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001 | 978-3-319-09117-4 | ||
003 | DE-He213 | ||
005 | 20200421112219.0 | ||
007 | cr nn 008mamaa | ||
008 | 140811s2015 gw | s |||| 0|eng d | ||
020 |
_a9783319091174 _9978-3-319-09117-4 |
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024 | 7 |
_a10.1007/978-3-319-09117-4 _2doi |
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050 | 4 | _aTK5102.9 | |
050 | 4 | _aTA1637-1638 | |
050 | 4 | _aTK7882.S65 | |
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_a621.382 _223 |
100 | 1 |
_aRoy, Suman Deb. _eauthor. |
|
245 | 1 | 0 |
_aSocial Multimedia Signals _h[electronic resource] : _bA Signal Processing Approach to Social Network Phenomena / _cby Suman Deb Roy, Wenjun Zeng. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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300 |
_aX, 176 p. 95 illus., 79 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _aWeb 2.x -- Media on the Web -- The World of Signals -- The Network and the Signal -- Detection - Needle in a Haystack -- Estimation - The Empirical Judgment -- Following Signal Trajectories -- Capturing Cross-Domain Ripples -- Socially-aware Media Applications -- Revelations from Social Multimedia Data -- Socio-Semantic Analysis -- Data Visualization: Gazing at Ripples. | |
520 | _aSocial Multimedia Signals is intended for those whose interest is to study the Social Web and develop automated tools to analyze it better. It is especially useful for researchers experienced with signal processing or multimedia analysis but have little exposure to social networks and social multimedia data. Those new to social multimedia should find the first chapters extremely useful to get a thorough look at how social data behaves. Conversely, social scientists should find useful the authors' introduction to several signal processing techniques that can be employed to manipulate large-scale social data. For those new to signal processing, Chapters 5, 6 and 7 will get readers underway with basic techniques for signal processing from social multimedia. Later chapters include a significant amount of material on machine learning for those interested in intelligent algorithms for the Social Web. The authors wrote this book in a balanced fashion, for multimedia researchers, social scientists, network scientists, data scientists who work with social web data, and professionals who use social media on a daily basis.   �         Explores how media popularity in one domain is determined by another domain; �         Presents a granular look at social networks: micro, meso, and macro; �         Examines finding hidden communities in social networks based on shared multimedia. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aIndustrial management. | |
650 | 0 | _aInput-output equipment (Computers). | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aSignal, Image and Speech Processing. |
650 | 2 | 4 | _aInput/Output and Data Communications. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aMedia Management. |
700 | 1 |
_aZeng, Wenjun. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319091167 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-09117-4 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c57313 _d57313 |