# Optimizing Shallow Networks for Binary Classification

arXiv: https://arxiv.org/abs/1905.10161 · HTML: https://mlsanity.com/paper/1905.10161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 1 of 1)

- [Xtreme Margin: A Tunable Loss Function for Binary Classification Problems](https://mlsanity.com/paper/2211.00176/) (2022)

## Abstract (arXiv)

Data driven classification that relies on neural networks is based on optimization criteria that involve some form of distance between the output of the network and the desired label. Using the same mathematical analysis, for a multitude of such measures, we can show that their optimum solution matches the ideal likelihood ratio test classifier. In this work we introduce a different family of optimization problems which is not covered by the existing approaches and, therefore, opens possibilities for new training algorithms for neural network based classification. We give examples that lead to algorithms that are simple in implementation, exhibit stable convergence characteristics and are antagonistic to the most popular existing techniques.