Online probabilistic label trees

Kalina Jasinska-Kobus; Marek Wydmuch; Devanathan Thiruvenkatachari; Krzysztof Dembczyński

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Title

Online probabilistic label trees

Authors

Kalina Jasinska-Kobus (WIiT) ^{[ 1 ][ D ]}
Marek Wydmuch (WIiT) ^{[ 1 ][ D ]}
Devanathan Thiruvenkatachari
Krzysztof Dembczyński (WIiT) ^{[ 1 ][ 2.3 ][ P ]}

^{[ 1 ]} Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | ^{[ D ]} phd student | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2021

Chapter type

chapter in monograph / paper

Publication language

english

Abstract

EN 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.

Pages (from - to)

1801 - 1809

URL

http://proceedings.mlr.press/v130/jasinska-kobus21a/jasinska-kobus21a.pdf

Book

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 13-15 April 2021

Presented on

24th International Conference on Artificial Intelligence and Statistics AISTATS 2021, 13-15.04.2023

License type

Copyright

Open Access Mode

publisher's website