On an Algorithm for the Recognition of Binary Codes

An algorithm is proposed for learning to classify objects described by a set of binary variables. The training is reduced to the selection of attributes of each class sufficient for the collection of examples. These attributes are sought among conjunctive variables describing the objects. In the selection each attribute is estimated by the number of examples possessing it. In recognition, the number of attributes of each class which the given object possesses is counted. The object is referred to the class for which this number is greatest. The algorithm has been successfully applied to the classification of oil bearing and water bearing strata.