Learning Decision Trees Using the Fourier Sprectrum (Extended Abstract)

  title={Learning Decision Trees Using the Fourier Sprectrum (Extended Abstract)},
  author={Eyal Kushilevitz and Yishay Mansour},
This work gives a polynomial time algcmithm for learning decision trees with respect tc~ the uniform distribution. (This algorithm uses membership queries.) The decision tree model that we consider is an extension of the traditional boolean decision tree model, and allows linear operations in each node (i.e. summation of a subset of the input variables over G1’(2)). We show how to learn in polynomial time any function that can be approximated (in norm L2) by a polynomially sparse function (i.e… CONTINUE READING
Highly Cited
This paper has 55 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 36 extracted citations

Derandomized Learning of Boolean Functions over Finite Abelian Groups

Int. J. Found. Comput. Sci. • 2001
View 9 Excerpts
Highly Influenced

Approximation from linear spaces and applications to complexity

Electronic Colloquium on Computational Complexity • 1996
View 8 Excerpts
Highly Influenced

On Using the Fourier Transform to Learn Disjoint DNF

Inf. Process. Lett. • 1994
View 7 Excerpts
Highly Influenced

A Striking Property of Genetic Code-like Transformations

Complex Systems • 2001
View 4 Excerpts
Highly Influenced

Sampling Boolean Functions over Abelian Groups and Applications

Applicable Algebra in Engineering, Communication and Computing • 2000
View 7 Excerpts
Highly Influenced

The Multivariate Hidden Number Problem

IACR Cryptology ePrint Archive • 2015
View 1 Excerpt

56 Citations

Citations per Year
Semantic Scholar estimates that this publication has 56 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…