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Frame theory in data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Frame theory in data science/ by Zhihua Zhang, Palle E. T. Jorgensen.
作者:	Zhang, Zhihua.
其他作者:	Jorgensen, Palle E. T.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	viii, 255 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining - Mathematics. -
電子資源:	https://doi.org/10.1007/978-3-031-49483-3
ISBN:	9783031494833

Frame theory in data science
Zhang, Zhihua.

Frame theory in data science[electronic resource] /by Zhihua Zhang, Palle E. T. Jorgensen. - Cham :Springer International Publishing :2024. - viii, 255 p. :ill., digital ;24 cm. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development,2522-8722. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development..

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.

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.

ISBN: 9783031494833

Standard No.: 10.1007/978-3-031-49483-3doiSubjects--Topical Terms:

2144379
Data mining
--Mathematics.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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