2020/07/08 by Kalina Jasinska-Kobus, Marek Wydmuch, Jasinska-Kobus, Kalina +5 · 1 citation
Computer Science · Mathematics · #Artificial intelligence #Attractiveness #Class (philosophy) #Classifier (UML) #Computer science #FOS: Computer and information sciences #Geography #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Machine learning #Mathematics #Multi-label classification #Probabilistic logic #Text and Document Classification Technologies #Train #Tree (set theory) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2007.04451
published in arXiv (Cornell University) (Cornell University) · Accepted at AISTATS 2021
openalex publication_date 2020/07/08 · arxiv created 2021/03/26 · arxiv updated 2021/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.