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Frame Theory in Data Science de Zhihua Zhang

Frame Theory in Data Science

Autor Zhihua Zhang, Palle E. T. Jorgensen
en Limba Engleză Paperback – 11 apr 2025
This book establishes brand-new frame theory and technical implementation in data science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis.
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Specificații

ISBN-13: 9783031494857
ISBN-10: 3031494857
Pagini: 264
Dimensiuni: 210 x 279 x 15 mm
Greutate: 0.65 kg
Editura: Springer

Cuprins

Abstract Frame Theory.- Fourier-type Frame Theory.- Bandlimited Framelet Theory.- Compactly Supported Framelet Theory.- Periodic Framelet Theory.- Spheroidal-type Frame Theory.- Big Data.- Climate Diagnosis.- Frame Neural Networks.

Notă biografică

Zhihua Zhang is a Taishan Distinguished Professor at Shandong University, China and is leading an interdisciplinary big data mining research group. His long-standing researches focus on big data, climate change mechanisms, environmental evolution and sustainability. He has published 8 books (2 with Elsevier, 5 with Springer, and 1 with DeGruyter) and more than 70 articles. He is a chief editor, associate editor, or editorial board member of many global known journals on applied mathematics, climate and environmental science, as well as the first-track chair and plenary speaker of Mediterranean Geosciences Union Annual Meeting.

Palle E.T. Jorgensen is a Professor at the University of Iowa. His prior academic/teaching positions include the University of Pennsylvania, Stanford University, and Aarhus University (Denmark.) He has authored more than 300 highly cited research papers, and more than 10 books. He has received numerous honors and awards, including in 2018 Jorgensen being the NSF/CBMS speaker, giving 10 lectures; titled Harmonic Analysis: Smooth and Non-smooth, published as vol. 128, in the AMS/CBMS book series. He is a frequent invited speaker, giving colloquium and conference presentations at universities in the US, and around the World. 

Textul de pe ultima copertă

This book establishes brand-new frame theory and technical implementation in data science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis. Given that data science is unanimously recognized as a core driver for achieving Sustainable Development Goals of the United Nations, these frame techniques bring fundamental changes to multi-channel data mining systems and support the development of digital Earth platforms. This book integrates the authors' frame research in the past twenty years and provides cutting-edge techniques and depth for scientists, professionals, and graduate students in data science, applied mathematics, environmental science, and geoscience. 

Caracteristici

The internationally first book on systematic frame theory and algorithms Novel applications of frame theory in big data, deep learning and climate diagnosis & prediction Includes the authors' frame research in the past twenty years