Difference between revisions of "Martins et al 2010"
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=== Method === | === Method === | ||
| − | The general loss function | + | The general loss function they use is: |
[[file:Martins et al 2010 Loss Function.png]] | [[file:Martins et al 2010 Loss Function.png]] | ||
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[[file:Martins et al 2010 Parameter Choices.png]] | [[file:Martins et al 2010 Parameter Choices.png]] | ||
| + | The function they minimize is the empirical risk with a regularizer: | ||
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| + | [[file:Martins et al 2010 Learning Problem.png]] [[file:Martins et al Relarizer.png]] | ||
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| + | [[file:Martins et al Regularize Coeff.png]] | ||
=== Experimental Result === | === Experimental Result === | ||
Revision as of 22:05, 1 October 2011
Citation and Online Link
A. F. T. Martins, K. Gimpel. N. A. Smith, E. P. Xing, P. M. Q. Aguiar, M. A. T. Figueiredo, 2010. Aggressive Online Learning of Structured Classifiers. Technical report CMU-ML-10-109.
Summary
This paper generalizes the loss function of CRFs, structured SVMs, structured perceptron, and Softmax-margin CRFs into a single loss function, and then derives an online learning algorithm that can be used to learn with that more general loss function. For the hinge loss, the learning algorithm reduces to MIRA.
Method
The general loss function they use is:
Different choices of and correspond to various well known loss functions. They are:
The function they minimize is the empirical risk with a regularizer:
Experimental Result
Related Papers
MIRA CRF Softmax-margin CRFs
In progress by User:Jmflanig
