# A Generalized Weighted Loss for SVC and MLP

## TL;DR

This paper introduces a generalized weighted loss function applicable to Support Vector Classification and Multi-layer Perceptron, improving performance by adaptively weighting errors without degrading standard methods.

## Contribution

It proposes a novel error weighting scheme that generalizes traditional loss functions for SVC and MLP, enhancing their robustness and accuracy.

## Key findings

- Error is never worse than standard loss methods
- Weighted loss often outperforms traditional approaches
- Applicable to both classification and regression models

## Abstract

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.