119 search results - page 10 / 24 » Training Conditional Random Fields Using Incomplete Annotati... |
CVPR
2009
IEEE
15 years 2 months ago
2009
IEEE
Many computer vision problems can be formulated in
a Bayesian framework with Markov Random Field (MRF)
or Conditional Random Field (CRF) priors. Usually, the
model assumes that ...
COLING
2010
13 years 2 months ago
2010
As one of the important tasks of SemEval Evaluation, Frame Semantic Structure Extraction based on the FrameNet has received much more attention in NLP field. This task is often di...
ACL
2006
13 years 9 months ago
2006
Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very...
ICCV
2003
IEEE
14 years 9 months ago
2003
IEEE
In this work we present Discriminative Random Fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the l...
PKDD
2007
Springer
14 years 1 months ago
2007
Springer
The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...