Self-improved gaps almost everywhere for the agnostic approximation of monomials

Given a learning sample, we focus on the hardness of finding monomials having low error, inside the interval bounded below by the smallest error achieved by a monomial (the best rule), and bounded above by the error of the default class (the poorest rule). It is well-known that when its lower bound is zero, it is an easy task to find, in linear time, a monomial with zero error. What we prove is that when this bound is not zero, regardless of the location of the default class in (0, 1/2), it becomes a huge complexity burden to beat significantly the default class. In fact, under some complexity-theoretical assumptions, it may already be hard to beat the trivial approximation ratios, even when relaxing the time complexity constraint to be quasi-polynomial or sub-exponential. Our results also hold with uniform weights over the examples. c © 2007 Elsevier B.V. All rights reserved.