Online Probabilistic Label Trees
Abstract
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels. OPLTs are characterized by low time and space complexity as well as strong theoretical guarantees. They can be used for online multi-label and multi-class classification, including the very challenging scenarios of one- or few-shot learning. We demonstrate the attractiveness of OPLTs in a wide empirical study on several instances of the tasks mentioned above.
Cite
Text
Jasinska-Kobus et al. "Online Probabilistic Label Trees." Artificial Intelligence and Statistics, 2021.Markdown
[Jasinska-Kobus et al. "Online Probabilistic Label Trees." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/jasinskakobus2021aistats-online/)BibTeX
@inproceedings{jasinskakobus2021aistats-online,
title = {{Online Probabilistic Label Trees}},
author = {Jasinska-Kobus, Kalina and Wydmuch, Marek and Thiruvenkatachari, Devanathan and Dembczynski, Krzysztof},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {1801-1809},
volume = {130},
url = {https://mlanthology.org/aistats/2021/jasinskakobus2021aistats-online/}
}