When is approximation by Gaussian networks necessarily a linear process?

DOI10.1016/j.neunet.2004.04.001zbMath1084.68104OpenAlexW2087948458WikidataQ44319882 ScholiaQ44319882MaRDI QIDQ1886595

Hrushikesh N. Mhaskar

Publication date: 18 November 2004

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2004.04.001

zbMATH Keywords

Gaussian networksConverse theoremsWeighted polynomial approximation Hermite polynomials

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

• Interpolation of scattered data: distance matrices and conditionally positive definite functions
• On the representation of band limited functions using finitely many bits
• Norm estimates for the inverses of a general class of scattered-data radial-function interpolation matrices
• Optimal nonlinear approximation
• On direct and converse theorems in the theory of weighted polynomial approximation
• Bounds on rates of variable-basis and neural-network approximation
• Comparison of worst case errors in linear and neural network approximation
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