ML Anthology

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

Ondrej Kuzelka, Yuyi Wang, Jan Ramon
IJCAI 2016 pp. 1655-1661
/ijcai/2016/kuzelka2016ijcai-bounds/

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. PDF

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Cite

Text

Kuzelka et al. "Bounds for Learning from Evolutionary-Related Data in the Realizable Case." International Joint Conference on Artificial Intelligence, 2016.

Markdown

[Kuzelka et al. "Bounds for Learning from Evolutionary-Related Data in the Realizable Case." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/kuzelka2016ijcai-bounds/)

BibTeX

@inproceedings{kuzelka2016ijcai-bounds,
  title     = {{Bounds for Learning from Evolutionary-Related Data in the Realizable Case}},
  author    = {Kuzelka, Ondrej and Wang, Yuyi and Ramon, Jan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {1655-1661},
  url       = {https://mlanthology.org/ijcai/2016/kuzelka2016ijcai-bounds/}
}