Approximate Location of Relevant Variables under the Crossover Distribution

Authors:
Peter Damaschke
Affiliations:
-
Venue:
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Year:
2001

Citing 9
Cited 0

A Fibonacci version of Kraft's inequality applied discrete unimodal search

SIAM Journal on Computing
Constant depth circuits, Fourier transform, and learnability

Journal of the ACM (JACM)
Efficient distribution-free learning of probabilistic concepts

Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution

Journal of Computer and System Sciences
More efficient PAC-learning of DNF with membership queries under the uniform distribution

COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Generalized Kraft's Inequality and Discrete $k$-Modal Search

SIAM Journal on Computing
A Formalism for Relevance and Its Application in Feature Subset Selection

Machine Learning
Adaptive Versus Nonadaptive Attribute-Efficient Learning

Machine Learning
Parallel Attribute-Efficient Learning of Monotone Boolean Functions

SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory

Quantified Score

Hi-index	0.02

Visualization

Abstract

Searching for genes involved in traits (e.g. diseases), based on genetic data, is considered from a computational learning perspective. This leads to the problem of learning relevant variables of functions from data sampled from a certain class of distributions generalizing the uniform distribution. The Fourier transform of Boolean functions is applied to translate the problem into searching for local extrema of certain functions of observables. We work out the combinatorial structure of this approach and illustrate its potential use.