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• W3098851804 abstract "Mathematical morphology (MM) is a theory of non-linear operators used for the processing and analysis of images. Morphological neural networks (MNNs) are neural networks whose neurons compute morphological operators. Dilations and erosions are the elementary operators of MM. From an algebraic point of view, a dilation and an erosion are operators that commute respectively with the supremum and infimum operations. In this paper, we present the textit{linear dilation-erosion perceptron} ($ell$-DEP), which is given by applying linear transformations before computing a dilation and an erosion. The decision function of the $ell$-DEP model is defined by adding a dilation and an erosion. Furthermore, training a $ell$-DEP can be formulated as a convex-concave optimization problem. We compare the performance of the $ell$-DEP model with other machine learning techniques using several classification problems. The computational experiments support the potential application of the proposed $ell$-DEP model for binary classification tasks." @default.
• W3098851804 created "2020-11-23" @default.
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• W3098851804 date "2020-11-11" @default.
• W3098851804 modified "2023-09-25" @default.
• W3098851804 title "Linear Dilation-Erosion Perceptron Trained Using a Convex-Concave Procedure" @default.
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