Learning with queries corrupted by classification noise Original Research Article

Kearns introduced the “statistical query” (SQ) model as a general method for producing learning algorithms which are robust against classification noise. We extend this approach in several ways in order to tackle algorithms that use “membership queries”, focusing on the more stringent model of “persistent noise”. The main ingredients in the general analysis are: