Online semi-supervised perception: Real-time learning without explicit feedback

• URL: http://arxiv.org/abs/2604.27562v1
• Date: Thu, 30 Apr 2026 08:14:13 GMT
• Title: Online semi-supervised perception: Real-time learning without explicit feedback
• Abstract summary: This paper proposes an algorithm for real-time learning without explicit feedback.<n>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.
• Score: 31.380197551243807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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