Implementation Issues in the Fourier Transform Algorithm

Yishay Mansour; Sigal Sahar

Implementation Issues in the Fourier Transform Algorithm

Yishay Mansour^*
, Sigal Sahar

^*Corresponding author for this work

School of Computer Science and AI

Tel Aviv University

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

The Fourier transform of boolean functions has come to play an important role in proving many important learnability results. We aim to demonstrate that the Fourier transform techniques are also a useful and practical algorithm in addition to being a powerful theoretical tool. We describe the more prominent changes we have introduced to the algorithm, ones that were crucial and without which the performance of the algorithm would severely deteriorate. One of the benefits we present is the confidence level for each prediction which measures the likelihood the prediction is correct.

Original language	English
Pages	260-266
Number of pages	7
State	Published - 1995
Event	8th International Conference on Neural Information Processing Systems, NIPS 1995 - Denver, United States Duration: 27 Nov 1995 → 2 Dec 1995

Conference

Conference	8th International Conference on Neural Information Processing Systems, NIPS 1995
Country/Territory	United States
City	Denver
Period	27/11/95 → 2/12/95

Funding

Funders
Israel Academy of Sciences and Humanities
Israel Science Foundation
Ministry of Science and Technology, Israel

Cite this

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title = "Implementation Issues in the Fourier Transform Algorithm",

abstract = "The Fourier transform of boolean functions has come to play an important role in proving many important learnability results. We aim to demonstrate that the Fourier transform techniques are also a useful and practical algorithm in addition to being a powerful theoretical tool. We describe the more prominent changes we have introduced to the algorithm, ones that were crucial and without which the performance of the algorithm would severely deteriorate. One of the benefits we present is the confidence level for each prediction which measures the likelihood the prediction is correct.",

author = "Yishay Mansour and Sigal Sahar",

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AU - Sahar, Sigal

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AB - The Fourier transform of boolean functions has come to play an important role in proving many important learnability results. We aim to demonstrate that the Fourier transform techniques are also a useful and practical algorithm in addition to being a powerful theoretical tool. We describe the more prominent changes we have introduced to the algorithm, ones that were crucial and without which the performance of the algorithm would severely deteriorate. One of the benefits we present is the confidence level for each prediction which measures the likelihood the prediction is correct.

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T2 - 8th International Conference on Neural Information Processing Systems, NIPS 1995

Y2 - 27 November 1995 through 2 December 1995

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Implementation Issues in the Fourier Transform Algorithm

Abstract

Conference

Funding

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Cite this