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 co-training 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.
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Li, H., Hu, Z., Wu, Y., Wu, F. (2007). MAPACo-Training: A Novel Online Learning Algorithm of Behavior Models. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_44
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DOI: https://doi.org/10.1007/978-3-540-76386-4_44
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