Search techniques for Fourier-based learning

Authors:
Adam Drake;Dan Ventura
Affiliations:
Computer Science Department, Brigham Young University;Computer Science Department, Brigham Young University
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 5
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Abstract

Fourier-based learning algorithms rely on being able to efficiently find the large coefficients of a function's spectral representation. In this paper, we introduce and analyze techniques for finding large coefficients. We show how a previously introduced search technique can be generalized from the Boolean case to the real-valued case, and we apply it in branch-and-bound and beam search algorithms that have significant advantages over the best-first algorithm in which the technique was originally introduced.