Learning regular sets from queries and counterexamples
Information and Computation
Improved learning of AC0 functions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
A technique for upper bounding the spectral norm with applications to learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
An O(nlog log n) learning algorithm for DNF under the uniform distribution
Journal of Computer and System Sciences
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
A practical generalization of Fourier-based learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Search techniques for Fourier-based learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Extracting a simplified view of design functionality based on vector simulation
HVC'06 Proceedings of the 2nd international Haifa verification conference on Hardware and software, verification and testing
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The Fourier transform of Boolean functions has received considerable attention in the last few years in the computational learning theory community, and has come to play an important role in proving many important learnability results. The aim of this work is to demonstrate that the Fourier transform techniques are also a useful and practical algorithm, in addition to having many interesting theoretical properties. In fact, this work was prompted by a genuine problem that was brought to our attention; researchers at a company were trying to come by a method to reverse-engineer a state-free controller. They had the capability of querying the controller on any input, thus setting them in the membership query model, in which the Fourier transform algorithm is set.In order to keep the algorithm run-time reasonable and still produce accurate hypotheses, we had to perform many optimizations. In the paper we discuss the more prominent optimizations, ones that were crucial and without which the performance of the algorithm would severely deteriorate. One of the benefits we present is the confidence level the algorithm produces in addition to the predictions. The confidence level measures the likelihood that the prediction is correct.