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Proceedings Paper

Learning Boolean functions with concentrated spectra
Author(s): Dustin G. Mixon; Jesse Peterson
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Paper Abstract

This paper discusses the theory and application of learning Boolean functions that are concentrated in the Fourier domain. We first estimate the VC dimension of this function class in order to establish a small sample complexity of learning in this case. Next, we propose a computationally efficient method of empirical risk minimization, and we apply this method to the MNIST database of handwritten digits. These results demonstrate the effectiveness of our model for modern classification tasks. We conclude with a list of open problems for future investigation.

Paper Details

Date Published: 24 August 2015
PDF: 8 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 95970C (24 August 2015); doi: 10.1117/12.2189112
Show Author Affiliations
Dustin G. Mixon, Air Force Institute of Technology (United States)
Jesse Peterson, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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