by Zhemin Zhu, Djoerd Hiemstra, Peter Apers, and Andreas Wombacher
Conditional Random Fields (CRFs) are discriminative undirected models which are globally normalized. Global normalization preserves CRFs from the label bias problem (LBP) which most local models suffer from. Recently proposed co-occurrence rate networks (CRNs) are also discriminative undirected models. In contrast to CRFs, CRNs are locally normalized. It was established that CRNs are immune to the LBP although they are local models. In this paper, we further compare these two models. The connection between CRNs and Copula are built in continuous case. Also their strength and weakness are further evaluated statistically by experiments.
The paper was presented at the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) in Bruges (Belgium) on 23-25 April 2014.