Building Function Approximators on top of Haar Scattering Networks
Machine Learning
2018-04-11 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1804.03236,
title = {Building Function Approximators on top of Haar Scattering Networks},
author = {Fernando Fernandes Neto},
journal= {arXiv preprint arXiv:1804.03236},
year = {2018}
}
Comments
7 pages, 5 figures, to appear in International Journal of Machine Learning and Computing, vol. 8 number 3