Incremental learning of approximations from positive data

Three different types of incremental learning are systematically studied: iterative learning, feedback inference, and bounded example-memory learning. In contrast to exact learning, where a learner is required to stabilize on a correct description of the target concept, approximate learning deals with scenarios in which a learner is successful if its final hypothesis describes a finite variant of the target concept. The considered models of approximate incremental learning are related to one another. The results achieved are compared to those obtained for exact learning.