Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
429866 | Journal of Computer and System Sciences | 2011 | 13 Pages |
Abstract
We show that unless NP = RP, it is hard to (even) weakly PAC-learn intersection of two halfspaces in Rn using a hypothesis which is a function of up to ℓ halfspaces (linear threshold functions) for any integer ℓ. Specifically, we show that for every integer ℓ and an arbitrarily small constant ε>0, unless NP = RP, no polynomial time algorithm can distinguish whether there is an intersection of two halfspaces that correctly classifies a given set of labeled points in Rn, or whether any function of ℓ halfspaces can correctly classify at most fraction of the points.
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