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Abstract: Two extensions of the Linial, Mansour, Nisan AC 0 learning algorithm are presented. The LMN method works when input examples are drawn uniformly. The new algorithms improve on theirs by performing well when given inputs drawn from unknown, mutually independent distributions. A variant of the one of the algorithms is conjectured to work in an even broader setting. 1 INTRODUCTION Linial, Mansour, and Nisan [LMN89] introduced the use of the Fourier transform to accomplish Boolean function... (Update)
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.... [f(x) 6= f(x Phi e i ) I D;i (f) The Fourier transform of Boolean functions over product distribution is defined as follows (see [FJS91]) First we define the inner product over the 2 dimensional vector space of all real valued functions over f0; 1g as follows: f; g) D...
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BibTeX entry: (Update)
M. L. Furst, J. C. Jackson, and S. W. Smith. Improved learning of AC 0 functions. In Proc. 4th Annu. Workshop on Comput. Learning Theory, pages 317--325. Morgan Kaufmann, San Mateo, CA, 1991. http://citeseer.ist.psu.edu/furst91improved.html More
@inproceedings{ furst91improved,
author = "Merrick L. Furst and Jeffrey C. Jackson and Sean W. Smith",
title = "Improved Learning of {AC} 0 Functions",
booktitle = "Computational Learing Theory",
pages = "317-325",
year = "1991",
url = "citeseer.ist.psu.edu/furst91improved.html" }
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