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Yun Fu has written 4 work(s)
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Product Description: This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos...read more
Hardcover:
9783319270029 | Springer Verlag, January 6, 2016, cover price $99.00 | About this edition: This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos.
Hardcover:
9783319119991 | Springer-Verlag New York Inc, November 19, 2014, cover price $109.00
Product Description: This book provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data...read more
Hardcover:
9783319054902 | Springer-Verlag New York Inc, April 7, 2014, cover price $109.00 | About this edition: This book provides a timely and unique survey of next-generation social computational methodologies.
Product Description: Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations. Manifold learning, a groundbreaking technique designed to tackle these issues of dimensionality reduction, finds widespread application in machine learning, neural networks, pattern recognition, image processing, and computer vision...read more
Hardcover:
9781439871096 | CRC Pr I Llc, December 20, 2011, cover price $115.95 | About this edition: Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-dimensional observations.
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