Non‐linear Trainable Classifier in IRd

Abstract

Describes a new kind of non‐linear trainable classifier, successfully tested in computer‐vision pattern recognition. Class regions are not described, as usually, through analytical means but as a reunion of standard sets. Defines the notion of E‐separability for the class regions in the feature space IRd considered as a metric space with a distance related to the Euclidean distance. Studies and proves the convergence of the decision regions to the class regions in this metric space. For a given E (is a member of) provides a stopping rule for the training phase. Then describes the working phase, showing how classification actually takes place. Finally, presents significant results.

Citation

Pascadi, M.A. and Pascadi, M.V. (1993), "Non‐linear Trainable Classifier in IRd", Kybernetes, Vol. 22 No. 1, pp. 13-21. https://doi.org/10.1108/eb005953