Pages that link to "Item:Q944076"

The following pages link to \(L^p\) approximation capability of RBF neural networks (Q944076):

Displaying 10 items.

• \(L^p\) Bernstein inequalities and inverse theorems for RBF approximation on \(\mathbb{R}^d\) (Q692566) (← links)
• Relaxed conditions for radial-basis function networks to be universal approximators. (Q1422258) (← links)
• Denseness of radial-basis functions in \(L^ 2(R^ n)\) and its applications in neural networks (Q1917815) (← links)
• A comment on ``Relaxed conditions for radial-basis function networks to be universal approximators'' (Q1932117) (← links)
• Approximation in weighted \(p\)-mean by RBF networks of Delsarte translates (Q2338756) (← links)
• Wiener's tauberian theorems for the Fourier-Bessel transformation and uniform approximation by RBF networks of Delsarte translates (Q2352197) (← links)
• Convergent decomposition techniques for training RBF neural networks (Q2746344) (← links)
• Constructive estimation of approximating of functions with Gaussian RBF neural network (Q2750993) (← links)
• Radial basis function neural networks of Hankel translates as universal approximators (Q5236750) (← links)
• A new variable shape parameter strategy for RBF approximation using neural networks (Q6162470) (← links)