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

• Type: Journal
• Year: 2006
• Where: ADCM
• Authors: Robert Schaback, J. Werner
• Keywords: ADCM 2006, Hilbert Space, Hilbert Space Norm, Reproducing Kernel Hilbert Space