Communications of the ACM
Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Fast learning of k-term DNF formulas with queries
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
THE SPECTRAL NORM OF FINITE FUNCTIONS
THE SPECTRAL NORM OF FINITE FUNCTIONS
Pseudorandom generators and learning algorithms for AC
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On the Fourier spectrum of monotone functions
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Learning using group representations (extended abstract)
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On the Fourier spectrum of monotone functions
Journal of the ACM (JACM)
Implementation Issues in the Fourier Transform Algorithm
Machine Learning
On Learning Monotone DNF under Product Distributions
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning intersections and thresholds of halfspaces
Journal of Computer and System Sciences - Special issue on FOCS 2002
On learning monotone DNF under product distributions
Information and Computation
Dynamic successive feed-forward neural network for learning fuzzy decision tree
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
On the structure of boolean functions with small spectral norm
Proceedings of the 5th conference on Innovations in theoretical computer science
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We present a general technique to upper bound the spectral norm of an arbitrary function. At the heart of our technique is a theorem which shows how to obtain an upper bound on the spectral norm of a decision tree given the spectral norms of the functions in the nodes of this tree. The theorem applies to trees whose nodes may compute any boolean functions. Applications are to the design of efficient learning algorithms and the construction of small depth threshold circuits (or neural nets). In particular, we present polynomial time algorithms for learning O(log n) clause DNF formulas and various classes of decision trees, all under the uniform distribution with membership queries.