Explanation-Based Learning for Image Understanding

Abstract

Existing prior domain knowledge represents a valuable source of information for image interpretation problems such as classifying handwritten characters. Such domain knowledge must be translated into a form understandable by the learner. Translation can be realized with Explanation-Based Learning (EBL) which provides a kind of dynamic inductive bias, combining domain knowledge and training examples. The dynamic bias formed by the interaction of domain knowledge with training examples can yield solution knowledge of potential higher quality than can be anticipated by the static bias designer without seeing training examples. We detail how EBL can be used to dynamically integrate domain knowledge, training examples, and the learning mechanism, and describe the two EBL approaches in (Sun & DeJong 2005a) and (Sun & DeJong 2005b).