Date of Award
1-1-2023
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School/Department
Department of Computer Science
Content Description
1 online resource (xiii, 138 pages) : illustrations (some color)
Dissertation/Thesis Chair
Charalampos Chelmis
Committee Members
Petko Bogdanov, Daphney-Stavroula Zois, Jeong-Hyon Hwang
Keywords
Artificial intelligence, Machine learning
Subject Categories
Computer Sciences
Abstract
One branch of machine learning algorithms is supervised learning, where the label is crucial for the learning model. Numerous algorithms have been proposed for supervised learning with different classification tasks. However, fewer works question the quality of the training labels. Training a learning model with noisy labels leads to decreased or untruthful performance. On the other hand, hierarchical multi-label classification (HMC) is one of the most challenging problems in machine learning because the classes in HMC tasks are hierarchically structured, and data instances are associated with multiple labels residing in a path of the hierarchy. Treating hierarchical tasks as flat and ignoring the hierarchical relationship between labels can degrade the model's performance. Therefore, in this thesis, we focus on learning from two types of difficult labels: noisy labels and hierarchical labels.
Recommended Citation
Qi, Wenting, "Learning from hierarchical and noisy labels" (2023). Legacy Theses & Dissertations (2009 - 2024). 3223.
https://scholarsarchive.library.albany.edu/legacy-etd/3223
