Kernel methods have emerged as a powerful tool in adaptive filtering and system identification, enabling the processing and modelling of complex, nonlinear relationships in dynamic systems. By mapping ...

This paper compares the method of Tikhonov regularization as advanced in 1963 with the more recent approach utilizing the theory of reproducing kernel Hilbert spaces (RKHS). The methods are shown to ...

For an arbitrary Hilbert space 𝓔, the Segal–Bargmann space 𝓗(𝓔) is the reproducing kernel Hilbert space associated with the kernel K(x, y) = exp(〈x, y〉) for x, y in 𝓔. If φ : 𝓔₁ → 𝓔₂ is a ...