# Asymptotic Error Rates for Point Process Classification

arXiv: https://arxiv.org/abs/2403.12531 · HTML: https://mlsanity.com/paper/2403.12531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

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

- [Asymptotic Classification Error for Heavy-Tailed Renewal Processes](https://mlsanity.com/paper/2408.10502/) (2024)

## Abstract (arXiv)

Point processes are finding growing applications in numerous fields, such as neuroscience, high frequency finance and social media. So classic problems of classification and clustering are of increasing interest. However, analytic study of misclassification error probability in multi-class classification has barely begun. In this paper, we tackle the multi-class likelihood classification problem for point processes and develop, for the first time, both asymptotic upper and lower bounds on the error rate in terms of computable pair-wise affinities. We apply these general results to classifying renewal processes. Under some technical conditions, we show that the bounds have exponential decay and give explicit associated constants. The results are illustrated with a non-trivial simulation.