Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
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The natural gene expression process evaluates the fitness of a DNA-string through a sequence of representation transformations. The genetic code defines one such transformation in this process. This paper shows that genetic code-like transformations introduce an interesting property in the representation of a genetic fitness function. It points out that such adaptive transformations can convert some functions with an exponentially large description in Fourier basis to one that is highly suitable for polynomial-size approximation. Such transformations can construct a Fourier representation with only a polynomial number of terms that are exponentially more significant than the rest when fitter chromosomes are given more copies through a redundant, equivalent representation. This is a very desirable property [2, 3] for efficient function-induction from data which is a fundamental problem in learning, data mining, and optimization.