In previous work, we described a knowledge-intensive inductive learning algorithm called abductive explanation-based learning (A-EBL) that uses background knowledge to improve the performance of a concept learner. A disadvantage of A-EBL is that it is not incremental. This article describes an alternative learning algorithm called IA-EBL that learns incrementallys IA-EBL replaces the set-cover-based learning algorithm of A-EBL with an extension of a perceptron learning algorithm. IA-EBL is in most other respects comparable to A-EBL, except that the output of the learning system can no longer be easily expressed as a logical theory. In this article, IA-EBL is described, analyzed according to Littlestone's model of mistake-bounded learnability, and finally compared experimentally to A-EBL. IA-EBL is shown to provide order-of-magnitude speedups over A-EBL in two domains when used in an incremental setting.