On Learning from Queries and Counterexamples in the Presence of Noise

Sakakibara, Y., On learning from queries and counterexamples in the presence of noise, Information Processing Letters 37 (1991) 279-284. Recently Angluin and Laird have introduced the classification noise process in the Valiant leamability model and proposed an interesting problem to explore the effect of noise in a situation that calls for queries as well as random sampling. In this paper, we present a general method to modify a polynomial-time learning algorithm from a sampling oracle and membership queries to compensate for random errors in the sampling and query responses.