Schematic classification problems and their solution

The necessity arises in a variety of tasks to classify items on the basis of the presence of one of a number of criterial sets of co-related feature values. Such sets are called class characteristics. Because such classification problems require the identification of characteristics on the basis of limited training information, they entail a difficult search problem. Consideration of the differences between the theoretical models underlying characteristic and volume pattern generators suggests a schematic approach. Schemata, sets of commonly co-occuring features values, are probabilistic indicators of class membership whenever the characteristics are unknown but the characteristic model prevails. Formal and algorithmic solutions to the classification problem when exemplars are simple (consist only of M feature or attribute values) are described. The relevance of these procedures to problems involving general (relational) data structures is also indicated.