Deep learning through sparse and low-rank modeling:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Oxford, United Kingdom
Academic Press, an imprint of Elsevier
[2019]
|
| Schriftenreihe: | Computer vision and pattern recognition series
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| Schlagworte: | |
| Online-Zugang: | FHI01 Volltext |
| Beschreibung: | 1 Online-Ressource (xvii, 277 Seiten) Illustrationen, Diagramme |
| ISBN: | 0128136596 9780128136591 |
| DOI: | 10.1016/C2017-0-00154-4 |
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| series2 | Computer vision and pattern recognition series |
| spelling | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang (Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States), Yun Fu (Department of Electrical and Computer Engineering and College of Computer and Information Science (Affiliated), Northeastern University, Boston, MA, United States), Thomas S. Huang (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States) London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Oxford, United Kingdom Academic Press, an imprint of Elsevier [2019] 1 Online-Ressource (xvii, 277 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Computer vision and pattern recognition series Deep learning (DE-588)1135597375 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Deep learning (DE-588)1135597375 s DE-604 Wang, Zhangyang (DE-588)1128658186 edt Fu, Yun (DE-588)1019266783 edt Huang, Thomas S. edt Erscheint auch als Druck-Ausgabe 978-0-12-813659-1 https://doi.org/10.1016/C2017-0-00154-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Deep learning through sparse and low-rank modeling Deep learning (DE-588)1135597375 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4143413-4 |
| title | Deep learning through sparse and low-rank modeling |
| title_auth | Deep learning through sparse and low-rank modeling |
| title_exact_search | Deep learning through sparse and low-rank modeling |
| title_exact_search_txtP | Deep learning through sparse and low-rank modeling |
| title_full | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang (Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States), Yun Fu (Department of Electrical and Computer Engineering and College of Computer and Information Science (Affiliated), Northeastern University, Boston, MA, United States), Thomas S. Huang (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States) |
| title_fullStr | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang (Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States), Yun Fu (Department of Electrical and Computer Engineering and College of Computer and Information Science (Affiliated), Northeastern University, Boston, MA, United States), Thomas S. Huang (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States) |
| title_full_unstemmed | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang (Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States), Yun Fu (Department of Electrical and Computer Engineering and College of Computer and Information Science (Affiliated), Northeastern University, Boston, MA, United States), Thomas S. Huang (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, United States) |
| title_short | Deep learning through sparse and low-rank modeling |
| title_sort | deep learning through sparse and low rank modeling |
| topic | Deep learning (DE-588)1135597375 gnd |
| topic_facet | Deep learning Aufsatzsammlung |
| url | https://doi.org/10.1016/C2017-0-00154-4 |
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