Deep learning through sparse and low-rank modeling:
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the tool...
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| Weitere beteiligte Personen: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Place of publication not identified]
Academic Press, an imprint of Elsevier
[2019]
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| Schriftenreihe: | Computer vision and pattern recognition series
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780128136607/?ar |
| Zusammenfassung: | Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. |
| Beschreibung: | Includes bibliographical references and index. - Vendor-supplied metadata |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9780128136607 012813660X |
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| spelling | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang, Yun Fu, Thomas S. Huang [Place of publication not identified] Academic Press, an imprint of Elsevier [2019] ©2019 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Machine learning Apprentissage automatique COMPUTERS ; General Wang, Zhangyang HerausgeberIn edt Fu, Yun HerausgeberIn edt Huang, Thomas S. 1936- HerausgeberIn edt 0128136596 Erscheint auch als Druck-Ausgabe 0128136596 |
| spellingShingle | Deep learning through sparse and low-rank modeling Machine learning Apprentissage automatique COMPUTERS ; General |
| 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_full | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang, Yun Fu, Thomas S. Huang |
| title_fullStr | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang, Yun Fu, Thomas S. Huang |
| title_full_unstemmed | Deep learning through sparse and low-rank modeling edited by Zhangyang Wang, Yun Fu, Thomas S. Huang |
| title_short | Deep learning through sparse and low-rank modeling |
| title_sort | deep learning through sparse and low rank modeling |
| topic | Machine learning Apprentissage automatique COMPUTERS ; General |
| topic_facet | Machine learning Apprentissage automatique COMPUTERS ; General |
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