(Enter summary)
Abstract: This work gives a polynomial time algorithm for learning decision trees with respect
to the uniform distribution. (This algorithm uses membership queries.) The decision tree
model that is considered is an extension of the traditional boolean decision tree model that
allows linear operations in each node (i.e., summation of a subset of the input variables
over GF (2)).
This paper shows how to learn in polynomial time any function that can be approximated
(in norm L 2 ) by a polynomially sparse... (Update)
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BibTeX entry: (Update)
Eyal Kushilevitz and Yishay Mansour. Learning decision trees using the fourier spectrum. In Proceedings of STOC '91, pages 455--464, may 1991. http://citeseer.ist.psu.edu/kushilevitz91learning.html More
@inproceedings{ kushilevitz91learning,
author = "Eyal Kushilevitz and Yishay Mansour",
title = "Learning decision trees using the {Fourier} spectrum",
pages = "455--464",
year = "1991",
url = "citeseer.ist.psu.edu/kushilevitz91learning.html" }
Citations (may not include all citations):
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