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

Branislav Kveton 1 Michal Valko 2 Mathai Phillipose 3 Ling Huang 4
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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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Submitted on : Sunday, November 20, 2011 - 10:47:43 PM
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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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