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Learning Decision Trees using the Fourier Spectrum (1991)  (Make Corrections)  (96 citations)
Eyal Kushilevitz, Yishay Mansour



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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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47   Annals of Pure and Applied Logic (context) - Ajtai, -formulae et al. - 1983
42   Learning decision trees from random examples (context) - Ehrenfeucht, Haussler - 1989
19   AC 0 functions and spectral norms (context) - Bruck, Smolensky et al. - 1990
18   A spectral lower bound technique for the size of decision tr.. (context) - Brandman, Hennessy et al. - 1990
13   A technique for upper bounding the spectral norm with applic.. - Bellare - 1992
7   Learning the fourier spectrum of probabilistic lists and tre.. (context) - Aiello, Mihail - 1991
2   Improved learning - Furst, Jackson et al.
1   Simple constructions of almost k-wisw independent random var.. (context) - Alon, Goldreich et al. - 1990
1   The use of Group representation in probability and statistic.. (context) - Diaconis - 1988



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