Learning algorithm based on modified structure of pattern classes

A procedure for classification and learning based on l-classifier is presented. Real data of five pattern classes have been used to demonstrate the applicability and efficiency of proposed procedure. Moment-invariant method is used for feature extraction, and dimensionality is reduced in order to maximize separability in reduced feature space. Experiment has shown that learning algorithm based on l-classifier has improved classification. Substitution procedure doesn´t change the number of initial patterns, so that optimality is achieved without expanding storage space and increasing computational complexity.