# On the Perceptron's Compression

arXiv: https://arxiv.org/abs/1806.05403 · HTML: https://mlsanity.com/paper/1806.05403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses](https://mlsanity.com/paper/2409.17932/) (2024)
- [Bound to Disagree: Generalization Bounds via Certifiable Surrogates](https://mlsanity.com/paper/2602.23128/) (2026)
- [Sample Compression for Self Certified Continual Learning](https://mlsanity.com/paper/2503.10503/) (2025)

## Abstract (arXiv)

We study and provide exposition to several phenomena that are related to the perceptron's compression. One theme concerns modifications of the perceptron algorithm that yield better guarantees on the margin of the hyperplane it outputs. These modifications can be useful in training neural networks as well, and we demonstrate them with some experimental data. In a second theme, we deduce conclusions from the perceptron's compression in various contexts.