Difference between revisions of "Globerson et al. ICML 2007. Exponentiated Gradient Algorithms for Log Linear Structured Prediction"
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It can be seen that the EG algorithm converges faster in terms of objective function and accuracy measures. | It can be seen that the EG algorithm converges faster in terms of objective function and accuracy measures. | ||
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+ | == Related Papers == | ||
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+ | The approach here is also similar to the use of EG algorithms for large margin structured classification in [[RelatedPaper::Bartlett et al NIPS 2004]]. |
Revision as of 16:51, 24 September 2011
Exponentiated gradient algorithms for log-linear structured prediction, by A. Globerson, T. Y Koo, X. Carreras, M. Collins. In Proceedings of the 24th international conference on Machine learning, 2007.
This Paper is available online [1].
Contents
Under construction
Summary
This paper describes an exponentiated gradient (EG) algorithm for training conditional log-linear models. Conditional log-linear models are used for several key structured prediction tasks such as NER, POS tagging, Parsing.
In this paper, they propose a fast and efficient algorithm for optimizing log-linear models such as CRFs.
The common practice of optimizing the conditional log likelihood of a CRF is often via conjugate-gradient or L-BFGS algorithms (Sha & Pereira, 2003), which typically would require at least one pass through the entire dataset before updating the weight vector. The author's approach here is an online algorithm based on exponentiated gradient updates (Kevin & Warmuth, 1997).
Brief description of the method
Consider a supervised learning setting with objects and corresponding labels , which maybe trees, sequences or other high dimensional structure. Also, assume we are given a function that maps pairs to feature vectors . Given a parameter vector , a conditional log-linear model defines a distribution over labels as:
where is a partition function.
The problem of learning from the training data is thus finding which maximizes the regularized log-likelihood:
where is the regularization parameter. The above equation has a convex dual which is derived in Lebanon and Lafferty NIPS 2001. With dual variables , and , we define:
where
The dual problem is thus
EG Algorithm
Given a set of distributions , the update equations are
where
and
Batch learning
At each iteration, is updated simultaneously with all (or subset of) the available training instances.
Online learning
At each iteration, we choose a single training instance, and update
Convergence rate of batch algorithm
To get within of the optimum parameters, we need iterations.
Experimental Result
The authors compared the performance of the EG algorithm to conjugated-gradient and L-BFGS methods.
Multiclass classification
The authors used a subset of the MNIST handwritten digits classification.
It can be seen that the EG algorithm converges considerably faster than the other methods.
Structured learning (dependency parsing)
The author used the Slovene data in UsesDataset:CoNLL-X Shared Task on Multilingual dependency parsing.
It can be seen that the EG algorithm converges faster in terms of objective function and accuracy measures.
Related Papers
The approach here is also similar to the use of EG algorithms for large margin structured classification in Bartlett et al NIPS 2004.