Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we introduce several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vector Classification and a regression net for Multi-layer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.
@article{arxiv.2302.12011,
title = {A Generalized Weighted Loss for SVC and MLP},
author = {Filippo Portera},
journal= {arXiv preprint arXiv:2302.12011},
year = {2023}
}