A pretopology-based supervised pattern classifier

Deals with the pretopological approach to the supervised pattern classification problem. An improved learning process which results in a reduced number of neighborhoods to be learned is proposed. We also present extensions concerning the use of different metrics in the /spl isin/-neighborhood definition in order to improve the class boundaries. Performances in terms of storage requirements, computation time and misclassification are provided and compared with the 1-nearest neighbor rule.