# Classification in asymmetric spaces via sample compression

arXiv: https://arxiv.org/abs/1909.09969 · HTML: https://mlsanity.com/paper/1909.09969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2019.

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

- [Weighted Distance Nearest Neighbor Condensing](https://mlsanity.com/paper/2310.15951/) (2023)

## Abstract (arXiv)

We initiate the rigorous study of classification in quasi-metric spaces. These are point sets endowed with a distance function that is non-negative and also satisfies the triangle inequality, but is asymmetric. We develop and refine a learning algorithm for quasi-metrics based on sample compression and nearest neighbor, and prove that it has favorable statistical properties.