  Greedy kernel approximation algorithms are successful techniques for sparse
and accurate data-based modelling and function approximation. Based on a recent
idea of stabilization of such algorithms in the scalar output case, we here
consider the vectorial extension built on VKOGA. We introduce the so called
$\gamma$-restricted VKOGA, comment on analytical properties and present
numerical evaluation on data from a clinically relevant application, the
modelling of the human spine. The experiments show that the new stabilized
algorithms result in improved accuracy and stability over the non-stabilized
algorithms.
