The use of explanations for similarity-based learning

Abstract

Due to the difficult nature of Machine Learning, it has often been looked at in the context of toy domains or in more realistic domains with simplifying assumptions. We propose an integrated learning approach that combines Explanation-Based and Similarity-Based Learning methods to make learning in an inherently complex domain feasible. We discuss the use of explanations for Similarity-Based Learning and present an example from a program which applies thee ideas to the domain of terrorist events.