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ICCPOL
2009
Springer
13 years 3 months ago
A Simple and Efficient Model Pruning Method for Conditional Random Fields
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
Hai Zhao, Chunyu Kit
BMCBI
2008
173views more  BMCBI 2008»
13 years 5 months ago
Extraction of semantic biomedical relations from text using conditional random fields
Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of a...
Markus Bundschus, Mathäus Dejori, Martin Stet...
ICMCS
2007
IEEE
143views Multimedia» more  ICMCS 2007»
13 years 11 months ago
Hidden Conditional Random Fields for Meeting Segmentation
Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a me...
Stephan Reiter, Björn Schuller, Gerhard Rigol...
PREMI
2009
Springer
13 years 12 months ago
Unsupervised Color Image Segmentation Using Compound Markov Random Field Model
Abstract. In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed s...
Sucheta Panda, P. K. Nanda
CVPR
2010
IEEE
13 years 11 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...