# Computed Decision Weights and a New Learning Algorithm for Neural Classifiers

arXiv: https://arxiv.org/abs/2209.08422 · HTML: https://mlsanity.com/paper/2209.08422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifiers](https://mlsanity.com/paper/1910.10122/) (2019)

## Abstract (arXiv)

In this paper we consider the possibility of computing rather than training the decision layer weights of a neural classifier. Such a possibility arises in two way, from making an appropriate choice of loss function and by solving a problem of constrained optimization. The latter formulation leads to a promising new learning process for pre-decision weights with both simplicity and efficacy.