Building Function Approximators on top of Haar Scattering Networks

2018-04-09 · Fernando Fernandes Neto

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.

📄 PDF Abstract BibTeX arXiv:1804.03236

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Econometrics

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