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Frame Theory in Data Science [electronic resource] / by Zhihua Zhang, Palle E. T. Jorgensen.

By: Contributor(s): Material type: TextSeries: Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable DevelopmentPublisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: VIII, 255 p. 7 illus., 6 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031494833
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • Q336
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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. .
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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. .

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