Online semi-supervised perception: Real-time learning without explicit feedback
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917450508926976 |
|---|---|
| author | Kveton, Branislav Valko, Michal Phillipose, Matthai Huang, Ling |
| author_facet | Kveton, Branislav Valko, Michal Phillipose, Matthai Huang, Ling |
| contents | This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27562 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Online semi-supervised perception: Real-time learning without explicit feedback Kveton, Branislav Valko, Michal Phillipose, Matthai Huang, Ling Machine Learning This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets. |
| title | Online semi-supervised perception: Real-time learning without explicit feedback |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.27562 |