Sciweavers

Share
DAGM
2008
Springer

Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation

8 years 11 months ago
Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation
We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the parameter likelihood gradient. We show, as opposed to [1][2], that these parameter learning methods can be improved and evaluate the resulting performance employing different inference techniques. We show that the approximation based on penalized pseudo-likelihood (PPL) in combination with the Maximum A Posteriori (MAP) inference yields results comparable to other state of the art approaches, while providing low complexity and advantages to formulating parameter learning as a convex optimization problem. Eventually, we demonstrate applicability on the task of detecting man-made structures in natural images. Key words: Approximate parameter learning, pseudo-likelihood, Conditional Random Field, Markov Random Field
Filip Korc, Wolfgang Förstner
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DAGM
Authors Filip Korc, Wolfgang Förstner
Comments (0)
books