# Asymptotic Supervised Predictive Classifiers under Partition Exchangeability

arXiv: https://arxiv.org/abs/2101.10950 · HTML: https://mlsanity.com/paper/2101.10950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 26 Jan 2021.

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

- [Bayes in Wonderland! Predictive supervised classification inference hits unpredictability](https://mlsanity.com/paper/2112.01880/) (2021)

## Abstract (arXiv)

The convergence of simultaneous and marginal predictive classifiers under partition exchangeability in supervised classification is obtained. The result shows the asymptotic convergence of these classifiers under infinite amount of training or test data, such that after observing umpteen amount of data, the differences between these classifiers would be negligible. This is an important result from the practical perspective as under the presence of sufficiently large amount of data, one can replace the simpler marginal classifier with computationally more expensive simultaneous one.