  In this paper we consider a problem of searching a space of predictive models
for a given training data set. We propose an iterative procedure for deriving a
sequence of improving models and a corresponding sequence of sets of non-linear
features on the original input space. After a finite number of iterations N,
the non-linear features become 2^N -degree polynomials on the original space.
We show that in a limit of an infinite number of iterations derived non-linear
features must form an associative algebra: a product of two features is equal
to a linear combination of features from the same feature space for any given
input point. Because each iteration consists of solving a series of convex
problems that contain all previous solutions, the likelihood of the models in
the sequence is increasing with each iteration while the dimension of the model
parameter space is set to a limited controlled value.
