---
title: "Bounds for Learning from Evolutionary-Related Data in the Realizable Case"
authors:
  - "Ondrej Kuzelka"
  - "Yuyi Wang"
  - "Jan Ramon"
doi: ""
url: "https://ijcai.org/proceedings/2016/237"
pdf: "https://ijcai.org/proceedings/2016/0237.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1655-1660"
track: ""
keywords:
---

# Bounds for Learning from Evolutionary-Related Data in the Realizable Case

Ondrej Kuzelka, Yuyi Wang, Jan Ramon

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1655-1660.

## Abstract

This paper deals with the generalization ability of classifiers trained from non-iid evolutionary-related data in which all training and testing examples correspond to leaves of a phylogenetic tree. For the realizable case, we prove PAC-type upper and lower bounds based on symmetries and matchings in such trees.