# A Generalized Weighted Loss for SVC and MLP

arXiv: https://arxiv.org/abs/2302.12011 · HTML: https://mlsanity.com/paper/2302.12011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 1 of 1)

- [A generalized quadratic loss for SVM and Deep Neural Networks](https://mlsanity.com/paper/2102.07606/) (2021)

## Abstract (arXiv)

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.