Difference between revisions of "Margin Infused Relaxed Algorithm"
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| − | This [[Category::method]] is used by [[RelatedPaper::Watanabe_et_al.,_EMNLP_2007._Online_Large-Margin_Training_for_Statistical_Machine_Translation|Watanabe et al., EMNLP 2007]] to train an MT system a with a very large number of features of the order of millions. The training step was performed using a specific algorithm called the Margin Infused Relaxed Algorithm (MIRA) proposed by [[RelatedPaper::Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer. Online Passive-Aggressive Algorithms. JMLR 7(Mar):551--585, 2006. | Crammer et al. | + | This [[Category::method]] is used by [[RelatedPaper::Watanabe_et_al.,_EMNLP_2007._Online_Large-Margin_Training_for_Statistical_Machine_Translation|Watanabe et al., EMNLP 2007]] to train an MT system a with a very large number of features of the order of millions. The training step was performed using a specific algorithm called the Margin Infused Relaxed Algorithm (MIRA) proposed by [[RelatedPaper::Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer. Online Passive-Aggressive Algorithms. JMLR 7(Mar):551--585, 2006. | Crammer et al., 2006]] |
== Summary == | == Summary == | ||
Revision as of 17:31, 29 September 2011
This method is used by Watanabe et al., EMNLP 2007 to train an MT system a with a very large number of features of the order of millions. The training step was performed using a specific algorithm called the Margin Infused Relaxed Algorithm (MIRA) proposed by Crammer et al., 2006
Summary
MIRA is an online large-margin training algorithm which updates the weight vector according to certain margin constraints and loss function. It is used to learn the weights of features after processing each training instance similar to [[UsesMethod:structured perceptron algorithm with an additional loss function in its update rule.
