Frank (Hrsg.) Nielsen
Geometric Structures of Information
Ebook (PDF Format)
This book focuses on information geometry manifolds of structured data/information and their advanced applications featuring new and fruitful interactions between several branches of science: information science, mathematics and physics. It addresses interrelations between different mathematical domains like shape spaces, probability/optimization & algorithms on manifolds, relational and discrete metric spaces, computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, and application…
Mehr
Beschreibung
This book focuses on information geometry manifolds of structured data/information and their advanced applications featuring new and fruitful interactions between several branches of science: information science, mathematics and physics. It addresses interrelations between different mathematical domains like shape spaces, probability/optimization & algorithms on manifolds, relational and discrete metric spaces, computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, and applications like geometries of audio-processing, inverse problems and signal processing.The book collects the most important contributions to the conference GSI'2017 - Geometric Science of Information.
CHF 177.00
Preise inkl. MwSt. und Versandkosten (Portofrei ab CHF 40.00)
Versandkostenfrei
Produktdetails
- ISBN: 978-3-030-02520-5
- EAN: 9783030025205
- Produktnummer: 29391012
- Verlag: Springer
- Sprache: Englisch
- Erscheinungsjahr: 2018
- Plattform: PDF
- Masse: 8'111 KB
Über den Autor
Frank Nielsen is Professor at the Laboratoire d'informatique de l'École polytechnique, Paris, France. His research aims at understanding the nature and structure of information and randomness in data, and exploiting algorithmically this knowledge in innovative imaging applications. For that purpose, he coined the field of computational information geometry (computational differential geometry) to extract information as regular structures whilst taking into account variability in datasets by grounding them in geometric spaces. Geometry beyond Euclidean spaces has a long history of revolutionizing the way we perceived reality. Curved spacetime geometry, sustained relativity theory and fractal geometry unveiled the scale-free properties of Nature. In the digital world, geometry is data-driven and allows intrinsic data analytics by capturing the very essence of data through invariance principles without being biased by such or such particular data representation.
28 weitere Werke von Frank (Hrsg.) Nielsen:
Ebook (PDF Format)
CHF 212.50
Bewertungen
0 von 0 Bewertungen
Anmelden
Keine Bewertungen gefunden. Seien Sie der Erste und teilen Sie Ihre Erkenntnisse mit anderen.