P06-1027 extending minimum conditional entropy regularization to the structured prediction
D09-1009 unlabeled data , we compare with entropy regularization ( ER ) . ER adds a term to the
P09-1041 Smith and Eisner ( 2007 ) apply entropy regularization to dependency parsing . The above
P08-1099 semi-supervised criteria , such as entropy regularization . Finally , their model is a
P06-1027 with this approach is that the entropy regularization term is not concave . To see
P06-1027 algorithm that exploits a form of entropy regularization on the unlabeled data . Specifically
P06-1027 based on extending the minimum entropy regularization framework to the structured prediction
P08-1099 the same label . ' In general , entropy regularization is fragile , and accuracy gains
D12-1125 optimizing over soft mappings , and use entropy regularization to drive those towards hard mappings
P06-1027 for CRFs , we extend the minimum entropy regularization framework of Grandvalet and Bengio
D11-1025 SSCRF ) can be implemented with entropy regularization ( ER ) . It extends the objective
P13-1076 S&T model trained using the entropy regularization ( ER ) criteria ( Jiao et al.
N13-1113 semi-supervised MaxEnt based on Entropy Regularization ( ER ) ( Vapnik , 1998 ; Jiao
D13-1117 intuition to techniques such as entropy regularization ( Grandvalet and Bengio , 2005
D12-1125 correspond to a mapping by adding an entropy regularization term : H -LSB- A -RSB- = −
P08-1099 semi-supervised CRF training is entropy regularization , initially proposed by Grandvalet
P14-1126 , as presented in the work on entropy regularization ( Jiao et al. , 2006 ; Mann and
P08-1099 transductive SVMs ( Joachims , 1999 ) , entropy regularization ( Grandvalet and Bengio , 2004
P08-1099 for CRF train - ing , including entropy regularization and expected gradient , showing
N07-2028 Fields </title> S Mann Abstract Entropy regularization is a straightforward and successful
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