AI RESEARCH
Online semi-supervised perception: Real-time learning without explicit feedback
arXiv CS.LG
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ArXi:2604.27562v1 Announce Type: new 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.