Deep Learning through Sparse and Low-Rank Modeling
Language: English
Published by Elsevier Inc Apr 2019, 2019
Series: Book 7 of 10 - Computer Vision and Pattern Recognition
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 201.49
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Add to basketItem description from seller
Neuware - 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.
Seller Inventory # 9780128136591
- Title
- Deep Learning through Sparse and Low-Rank Modeling
- Author
- Zhangyan Wang
- Publisher
- Elsevier Inc Apr 2019
- Publication year
- 2019
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128136596
- ISBN 13
- 9780128136591
- Item weight
- 514 grams
- Dimensions
- 235x191x16 mm
- Series
- Book 7 of 10: Computer Vision and Pattern Recognition
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.
"Synopsis" may belong to another edition of this title.
About the Author
Prof. Yu Fu is a Full Professor at College of Food Science, Southwest University, China. He earned his PhD degree in Food Science from Aarhus University and completed postdoctoral research at University of Copenhagen. He has also served as a visiting scholar at the University of Manitoba and the University of Aberdeen. Dr. Fu has led several research projects, including National Natural Science Foundation grants, National Key R&D Program, etc. He has published over 100 peer-reviewed papers as first or corresponding author in internationally respected journals such as Journal of Advanced Research, Trends in Food Science & Technology, Journal of Agricultural and Food Chemistry, and Food Chemistry. He has contributed to eight English academic books and holds ten national invention patents. He also serves the scientific community in numerous professional and editorial roles, including member of Youth Working Committee of Chinese Association of Animal Products Processing, expert of Technical Advisory Committee of Chongqing Agricultural Product Processing Association, Editor-in-Chief of International Journal of Food Studies, Editor for Trends in Food Science & Technology, Academic Editor for Journal of Food Biochemistry, Deputy Editor for International Journal of Food Science & Technology. He was listed among Elsevier’s “Top 2% Scientists (2023–2025) and has received awards including the ACU Early Career Award, Foods Outstanding Young Scholar Award, Excellent Instructor Award for International College Students’ Innovation Competition (Gold award), EFFoST “PhD Student of the Year award, and the Best Oral Presentation Award at the ICoMST.
Thomas S. Huang received his B.S. Degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, China; and his M.S. and Sc.D. Degrees in Electrical Engineering from the Massachusetts Institute of Technology, Cambridge, Massachusetts. He was on the Faculty of the Department of Electrical Engineering at MIT from 1963 to 1973; and on the Faculty of the School of Electrical Engineering and Director of its Laboratory for Information and Signal Processing at Purdue University from 1973 to 1980.
Dr. Huang's professional interests lie in the broad area of information technology, especially the transmission and processing of multidimensional signals. He has published 21 books, and over 600 papers in Network Theory, Digital Filtering, Image Processing, and Computer Vision. Among his many honors and awards: Honda Lifetime Achievement Award, IEEE Jack Kilby Signal Processing Medal, and the King-Sun Fu Prize of the International Association for Pattern Recognition.
"About the title" may belong to another edition of this title.
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