Search Techniques for Fourier-Based Learning
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. Adam Drake, Dan Ventura
Cite
Text
Drake and Ventura. "Search Techniques for Fourier-Based Learning." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Drake and Ventura. "Search Techniques for Fourier-Based Learning." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/drake2009ijcai-search/)BibTeX
@inproceedings{drake2009ijcai-search,
title = {{Search Techniques for Fourier-Based Learning}},
author = {Drake, Adam and Ventura, Dan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {1040-1045},
url = {https://mlanthology.org/ijcai/2009/drake2009ijcai-search/}
}