Learning an unknown halfspace (also called a perceptron) from labeled examples is one of the classic problems in machine learning. In the noise-free case, when a halfspace consistent with all the training examples exists, the problem can be solved in polynomial time using linear programming. However, under the promise that a halfspace consistent with a fraction (1-\varepsilon ) of the examples exists (for some small constant \varepsilon > 0), it was not known how to efficiently find a halfspace that is correct on even 51% of the examples. Nor was a hardness result that ruled out getting agreement on more than 99.9% of the examples known.

In this work, we close this gap in our understanding, and prove that even a tiny amount of worst-case noise makes the problem of learning halfspaces intractable in a strong sense. Specifically, for arbitrary \varepsilon, \delta \ge 0, we prove that given a set of examples-label pairs from the hypercube a fraction (1-\varepsilon ) of which can be explained by a halfspace, it is NP-hard to find a halfspace that correctly labels a fraction (1/2 + \delta ) of the examples.

The hardness result is tight since it is trivial to get agreement on 1/2 the examples. In learning theory parlance, we prove that weak proper agnostic learning of halfspaces is hard. This settles a question that was raised by Blum et al in their work on learning halfspaces in the presence of random classification noise [7], and in some more recent works as well. Along the way, we also obtain a strong hardness for another basic computational problem: solving a linear system over the rationals.