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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(3): 262-267 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.3.697

Building Function Approximators on top of Haar Scattering Networks

Fernando Fernandes Neto
Abstract—In this article, we propose building generalpurpose 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.

Index Terms—Scattering transforms, feature extraction, geometric learning, machine learning.

Fernando Fernandes Neto is with the University of São Paulo, São Paulo, Brazil (e-mail: fernando_fernandes_neto@usp.br).


Cite: Fernando Fernandes Neto, "Building Function Approximators on top of Haar Scattering Networks," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 262-267, 2018.

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