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Agnostic Learning of Disjunctions on Symmetric Distributions
[article]
2015
arXiv
pre-print
We consider the problem of approximating and learning disjunctions (or equivalently, conjunctions) on symmetric distributions over {0,1}^n. Symmetric distributions are distributions whose PDF is invariant under any permutation of the variables. We give a simple proof that for every symmetric distribution D, there exists a set of n^O((1/ϵ)) functions S, such that for every disjunction c, there is function p, expressible as a linear combination of functions in S, such that p ϵ-approximates c in
arXiv:1405.6791v2
fatcat:fwygrqmhyremxpwedlpz5hqgmq