Noise-tolerant distribution-free learning of general geometric concepts

We present an efficient algorithm for PAC-learning a very general class of geometric concepts over Rd for fixed d. More specifically, let T be any set of s halfspaces. Let x = (x1,..., Xd) be an arbitrary point in Rd. With each t ∈ f we associate a boolean indicator function It(x) which is 1 if and only if x is in the halfspace t. The concept class, Cds, that we study consists of all concepts formed by any boolean function over It1,..., Its for ti ∈ T. This concept class is much more general than any geometric concept class known to be PAC-learnable. Our results can be easily extended to efficiently learn any boolean combination of a polynomial number of concepts selected from any concept class C over Rd given that the VC-dimension of C has dependence only on d (and is thus constant for any constant d), and there is a polynomial time algorithm to determine if there is a concept from C consistent with a given set of labeled examples. We also present a statistical query version of our algorithm that can tolerate random classification noise for any noise rate strictly less than 1/2. FinaEy we present a generalization of the standard e-net result of Haussler and Welzl [25] and apply it to give an alternative noise-tolerant algorithm for d = 2 based on geometric subdivisions. · 1996 ACM.