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2006

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

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Linearly constrained reconstruction of functions by kernels with applications to machine learning
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
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where ADCM
Authors Robert Schaback, J. Werner
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