A Generalized Weighted Loss for SVC and MLP

Autor:	Portera, Filippo
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 3 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.12011 Zobrazit plný text záznamu View this record from Arxiv