An upper bound on the sample complexity of PAC-learning halfspaces with respect to the uniform distribution

We show that halfspaces in n dimensions can be PAC-learned with respect to the uniform distribution with accuracy e and confidence δ using O(1/e- (n + log 1/δ)) examples.