  A model-independent or non-parametric approach for modeling a database has
been widely used in cosmology. In these scenarios, the data has been used
directly to reconstruct an underlying function. In this work, we introduce a
novel semi-model-independent method to do the task. The new approach not only
removes some drawbacks of previous methods but also has some remarkable
advantages. We combine the well-known Gaussian linear model with a neural
network and introduce a procedure for the reconstruction of an arbitrary
function. In the scenario, the neural network produces some arbitrary base
functions which subsequently are fed to the Gaussian linear model. Given a
prior distribution on the free parameters, the Gaussian linear model provides a
close form for the posterior distribution as well as the Bayesian evidence. In
addition, contrary to other methods, it is straightforward to compute the
uncertainty.
