Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of estimating the Fourier spectra of Boolean functions using noisy non-uniformly drawn random examples is considered. In particular, arbitrary product distributions on the n-dimensional attribute vectors are assumed. The attributes are disturbed by noise also following a product distribution. Under these conditions the problem of estimating the Fourier spectra is considered. A general expression is derived that allows the construction of estimators of the Fourier spectra. This results can be applied to learn functions that are concentrated on the lor part of their spectra. As an application of the presented results an algorithm is shown that infers the relevant variables of so-called 1-low Boolean juntas.
['Steffen Schober', 'Martin Bossert']
I am writing a research paper on a idea On spectral estimators of Boolean functions
[{"from": "human", "value": "I am writing a research paper on a idea On spectral estimators of Boolean functions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of estimating the Fourier spectra of Boolean functions using noisy non-uniformly drawn random examples is considered. In particular, arbitrary product distributions on the n-dimensional attribute vectors are assumed. The attributes are disturbed by noise also following a product distribution. Under these conditions the problem of estimating the Fourier spectra is considered. A general expression is derived that allows the construction of estimators of the Fourier spectra. This results can be applied to learn functions that are concentrated on the lor part of their spectra. As an application of the presented results an algorithm is shown that infers the relevant variables of so-called 1-low Boolean juntas."}]