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Conference Papers Year : 2010

Online Semi-Supervised Perception: Real-Time Learning without Explicit Feedback

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Michal Valko
Mathai Phillipose
  • Function : Author
Ling Huang

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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Dates and versions

hal-00642999 , version 1 (20-11-2011)

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Cite

Branislav Kveton, Michal Valko, Mathai Phillipose, Ling Huang. Online Semi-Supervised Perception: Real-Time Learning without Explicit Feedback. 4th IEEE Online Learning for Computer Vision Workshop, Jun 2010, San Francisco, United States. ⟨10.1109/CVPRW.2010.5543877⟩. ⟨hal-00642999⟩
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