Abstract
We give several properties of the reproducing kernel Hilbert space induced by the Gaussian kernel, along with their implications for recent results in the complexity of the regularized least square algorithm in learning theory.
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Communicated by Wolfgang Dahmen.
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Minh, H.Q. Some Properties of Gaussian Reproducing Kernel Hilbert Spaces and Their Implications for Function Approximation and Learning Theory. Constr Approx 32, 307–338 (2010). https://doi.org/10.1007/s00365-009-9080-0
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DOI: https://doi.org/10.1007/s00365-009-9080-0
- Reproducing kernel Hilbert spaces
- Gaussian kernel
- Eigenvalues
- Learning theory
- Regularized least square algorithm