A practical generalization of Fourier-based learning

Authors:
Adam Drake;Dan Ventura
Affiliations:
Brigham Young University, Provo, UT;Brigham Young University, Provo, UT
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

Citing 4
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Abstract

This paper presents a search algorithm for finding functions that are highly correlated with an arbitrary set of data. The functions found by the search can be used to approximate the unknown function that generated the data. A special case of this approach is a method for learning Fourier representations. Empirical results demonstrate that on typical real-world problems the most highly correlated functions can be found very quickly, while combinations of these functions provide good approximations of the unknown function.