MAPACo-training: a novel online learning algorithm of behavior models

Authors:
Heping Li;Zhanyi Hu;Yihong Wu;Fuchao Wu
Affiliations:
National Laboratory of Pattern Recognition and Digital Content Technology Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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Abstract

The traditional co-training algorithm, which needs a great number of unlabeled examples in advance and then trains classifiers by iterative learning approach, is not suitable for online learning of classifiers. To overcome this barrier, we propose a novel semi-supervised learning algorithm, called MAPACo-Training, by combining the cotraining with the principle of Maximum A Posteriori adaptation. This MAPACo-Training algorithm is an online multi-class learning algorithm, and has been successfully applied to online learning of behaviors modeled by Hidden Markov Model. The proposed algorithm is tested with the Li's database as well as Schuldt's dataset.