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Complexity Theoretic Limitations on Learning Halfspaces
[article]
2016
arXiv
pre-print
We study the problem of agnostically learning halfspaces which is defined by a fixed but unknown distribution D on Q^n×{± 1}. We define Err_HALF(D) as the least error of a halfspace classifier for D. A learner who can access D has to return a hypothesis whose error is small compared to Err_HALF(D). Using the recently developed method of the author, Linial and Shalev-Shwartz we prove hardness of learning results under a natural assumption on the complexity of refuting random K-XOR formulas. We
arXiv:1505.05800v2
fatcat:udprem4j7zdvvksswzzeowmwku