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ISBN: 9780128136607 012813660X 0128136596 9780128136591 Year: 2019 Publisher: London : Academic Press,
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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. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications
ISBN: 9812776958 9789812776952 9789812380913 9812380914 Year: 2002 Publisher: River Edge, NJ World Scientific
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This volume of original papers has been assembled to honor the achievements of Professor Thomas S Huang in the area of image processing and image analysis. Professor Huang's life of inquiry has spanned a number of decades as his work on imaging problems began in 1960's. Over these 40 years, he has made many fundamental and pioneering contributions to nearly every area of this field. Professor Huang has received numerous Awards, including the prestigious Jack Kilby Signal Processing Medal from IEEE. He has been elected to the National Academy of Engineering, and named Fellow of IEEE, Fellow of
Image processing --- Imaging systems. --- Computer vision. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Pattern recognition systems --- Radar --- Remote sensing --- Television --- Scanning systems --- Digital image processing --- Digital electronics --- Digital techniques. --- Equipment and supplies
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