Almost optimal estimates for approximation and learning by radial basis function networks
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Publication:2251472
DOI10.1007/s10994-013-5406-zzbMath1320.68141OpenAlexW2110456244MaRDI QIDQ2251472
Yuanhua Rong, Xia Liu, Shao-Bo Lin, Zong Ben Xu
Publication date: 14 July 2014
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-013-5406-z
rate of convergenceradial basis function networksmachine learningempirical risk minimizationapproximation of differentiable multivariate functions
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Cites Work
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- The essential rate of approximation for radial function manifold
- On the tractability of multivariate integration and approximation by neural networks
- A bound on the approximation order of surface splines
- Lower bounds for approximation by MLP neural networks
- Entropy and the combinatorial dimension
- Improved multiquadric method for elliptic partial differential equations via PDE collocation on the boundary
- On lower bounds in radial basis approximation
- A distribution-free theory of nonparametric regression
- Best choices for regularization parameters in learning theory: on the bias-variance problem.
- On best approximation of classes by radial functions
- On quasi-interpolation by radial basis functions with scattered centres
- Error estimates and condition numbers for radial basis function interpolation
- Approximation by radial basis functions with finitely many centers
- The rate of approximation of Gaussian radial basis neural networks in continuous function space
- Essential rate for approximation by spherical neural networks
- Optimal rates for the regularized least-squares algorithm
- Approximation methods for supervised learning
- Approximation and learning by greedy algorithms
- Approximation in learning theory
- Approximation by neural networks and learning theory
- Approximation with polynomial kernels and SVM classifiers
- Pseudo-dimension and entropy of manifolds formed by affine-invariant dictionary
- On the mathematical foundations of learning
- Optimal rate of the regularized regression learning algorithm
- A radial basis function method for the shallow water equations on a sphere
- Universal approximation bounds for superpositions of a sigmoidal function
- Lower bounds for multivariate approximation by affine-invariant dictionaries
- Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks
- Scattered Data Approximation
- Error bounds for approximation with neural networks
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