This paper is concerned with knowledge representation issues in machine learning. In particular, it presents a representation language that supports a hybrid analytical and similarity-based classification scheme. Analytical classification is produced using a KL-ONE-like term-subsumption strategy, while similarity-based classification is driven by generalizations induced from a training set by an unsupervised learning procedure. This approach can be seen as providing an inductive bias to the learning procedure, thereby shortening the required training phase, and reducing the brittleness of the induced generalizations.
['Marc B. Vilain', 'Phyllis Koton', 'Melissa P. Chase']
On analytical and similarity-based classification