We propose a new loss function for the discriminative
learning of Markov random fields, which is an intermediate
loss function between sequential loss and pointwise loss.
We show this loss function has “Markov property”, that is,
the importance of correct labeling for a particular position
depends on the numbers of the correct labels around there.
This property works to keep local consistencies and is useful
for optimizing systems identifying structural segments, such
as information extraction systems.
By: Yuta Tsuboi and Hisashi Kashima
Published in: RT0660 in 2007
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