Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting
This thesis focuses on two fundamental machine learning problems:unsupervised learning, where no label information is available, and semi-supervised learning, where a small amount of labels are given in addition to unlabeled data. These problems arise in many real word applications, such as Web anal...
| Main Author: | |
|---|---|
| Language: | en |
| Published: |
2007
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10012/3165 |