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ICML
2004
IEEE
16 years 12 days ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
ICDM
2007
IEEE
157views Data Mining» more  ICDM 2007»
15 years 1 months ago
Training Conditional Random Fields by Periodic Step Size Adaptation for Large-Scale Text Mining
For applications with consecutive incoming training examples, on-line learning has the potential to achieve a likelihood as high as off-line learning without scanning all availabl...
Han-Shen Huang, Yu-Ming Chang, Chun-Nan Hsu
ICDAR
2007
IEEE
15 years 6 months ago
Document Image Segmentation Using a 2D Conditional Random Field Model
This work relates to the implementation of a 2D conditional random field model in the context of document image analysis. Our model makes it possible to take variability into acco...
Stéphane Nicolas, J. Dardenne, Thierry Paqu...
ICML
2010
IEEE
15 years 20 days ago
Conditional Topic Random Fields
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assig...
Jun Zhu, Eric P. Xing
ICML
2006
IEEE
16 years 12 days ago
Accelerated training of conditional random fields with stochastic gradient methods
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...