  In this article we propose building general-purpose function approximators on
top of Haar Scattering Networks. We advocate that this architecture enables a
better comprehension of feature extraction, in addition to its implementation
simplicity and low computational costs. We show its approximation and feature
extraction capabilities in a wide range of different problems, which can be
applied on several phenomena in signal processing, system identification,
econometrics and other potential fields.
