On Kolmogorov's Superposition Theorem and its Applications
A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
Autor*innen
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- 192 Seiten
- 7 Lesestunden
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The book introduces a Regularization Network approach utilizing Kolmogorov's superposition theorem to reconstruct higher-dimensional continuous functions from discrete data points. It presents a new constructive proof of the theorem and explores its various versions, linking them to well-known approximation methods and Neural Networks. The work addresses the challenge of the curse of dimensionality, proposing a nonlinear model for function reconstruction within a reproducing kernel Hilbert space. It includes verification and analysis through numerous numerical examples.
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On Kolmogorov's Superposition Theorem and its Applications, Jürgen Braun
- Sprache
- Erscheinungsdatum
- 2010
- Einband
- (Paperback)
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- Titel
- On Kolmogorov's Superposition Theorem and its Applications
- Untertitel
- A Nonlinear Model for Numerical Function Reconstruction from Discrete Data Sets in Higher Dimensions
- Sprache
- Englisch
- Autor*innen
- Jürgen Braun
- Erscheinungsdatum
- 2010
- Einband
- Paperback
- Seitenzahl
- 192
- ISBN13
- 9783838116372
- Reihe
- Schlagwörter
- Sachbücher, Lehrbücher, Wissenschaft & Mathematik, Natur, Universitätslehrbücher, Wissenschaft, Mathematik, Mathematik und Logik
- Bewertung
- 3 von 5 Sternen
- Beschreibung
- The book introduces a Regularization Network approach utilizing Kolmogorov's superposition theorem to reconstruct higher-dimensional continuous functions from discrete data points. It presents a new constructive proof of the theorem and explores its various versions, linking them to well-known approximation methods and Neural Networks. The work addresses the challenge of the curse of dimensionality, proposing a nonlinear model for function reconstruction within a reproducing kernel Hilbert space. It includes verification and analysis through numerous numerical examples.