Elements of information theory
Elements of information theory
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Mirror descent and nonlinear projected subgradient methods for convex optimization
Operations Research Letters
Laplace maximum margin Markov networks
Proceedings of the 25th international conference on Machine learning
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Primal sparse Max-margin Markov networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Training Global Linear Models for Chinese Word Segmentation
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Accelerating the annotation of sparse named entities by dynamic sentence selection
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Using LDA to detect semantically incoherent documents
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Cutting-plane training of structural SVMs
Machine Learning
Structured prediction with reinforcement learning
Machine Learning
Training parsers by inverse reinforcement learning
Machine Learning
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Confidence-weighted linear classification for text categorization
The Journal of Machine Learning Research
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Conditional log-linear models are a commonly used method for structured prediction. Efficient learning of parameters in these models is therefore an important problem. This paper describes an exponentiated gradient (EG) algorithm for training such models. EG is applied to the convex dual of the maximum likelihood objective; this results in both sequential and parallel update algorithms, where in the sequential algorithm parameters are updated in an online fashion. We provide a convergence proof for both algorithms. Our analysis also simplifies previous results on EG for max-margin models, and leads to a tighter bound on convergence rates. Experiments on a large-scale parsing task show that the proposed algorithm converges much faster than conjugate-gradient and L-BFGS approaches both in terms of optimization objective and test error.