Linearly constrained reconstruction of functions by kernels with applications to machine learning

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This paper investigates the approximation of multivariate functions from data via linear combinations of translates of a positive definite kernel from a reproducing kernel Hilbert space. If standard interpolation conditions are relaxed by Chebyshev

Robert Schaback, J. Werner

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ADCM 2006 | Hilbert Space | Hilbert Space Norm | Reproducing Kernel Hilbert Space |

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Added	10 Dec 2010
Updated	10 Dec 2010
Type	Journal
Year	2006
Where	ADCM
Authors	Robert Schaback, J. Werner

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Linearly constrained reconstruction of functions by kernels with applications to machine learning

ADCM 2006 | Hilbert Space | Hilbert Space Norm | Reproducing Kernel Hilbert Space |

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