HARDNESS OF LEARNING HALFSPACES WITH NOISE
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 - epsilon) of the examples exists (for some small constant epsilon > 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 epsilon, delta > 0, we prove that given a set of examples-label pairs from the hypercube, a fraction (1 - epsilon) 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 [Algorithmica, 22 (1998), pp. 35-52], and raised by authors of some more recent works as well. Along the way, we also obtain a strong hardness result for another basic computational problem: solving a linear system over the rationals.
perceptron hardness of approximation computational learning