%0 Conference Proceedings
%T A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes
%A Francesco Ciompi
%A Oriol Pujol
%A Petia Radeva
%B 20th International Conference on Pattern Recognition
%D 2010
%@ 1051-4651
%@ 978-1-4244-7542-1
%F Francesco Ciompi2010
%O MILAB;HUPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1365), last updated on Thu, 18 Jan 2018 12:00:12 +0100
%X We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems.
%U http://dx.doi.org/10.1109/ICPR.2010.179
%P 710–713