In this work, we briefly revise the reduced dilation-erosion perceptron (r-DEP) models for binary classification tasks. Then, we present the so-called linear dilation-erosion perceptron (l-DEP), in which a linear transformation is applied before the application of the morphological operators. Furthermore, we propose to train the l-DEP classifier by minimizing a regularized hinge-loss function subject to concave-convex restrictions. A simple example is given for illustrative purposes.
Cite
@article{arxiv.2011.05989,
title = {Linear Dilation-Erosion Perceptron for Binary Classification},
author = {Angelica Lourenço Oliveira and Marcos Eduardo Valle},
journal= {arXiv preprint arXiv:2011.05989},
year = {2020}
}
Comments
2 pages, 1 figure, XV Encontro Cient\'ifico de P\'os-Graduandos do IMECC