Statistical methods for speech recognition
Statistical methods for speech recognition
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Convex Optimization
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Transductive support vector machines for structured variables
Proceedings of the 24th international conference on Machine learning
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
Crouching Dirichlet, hidden Markov model: unsupervised POS tagging with context local tag generation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Incorporating the loss function into discriminative clustering of structured outputs
IEEE Transactions on Neural Networks
Learning from partially annotated sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
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We present a new unsupervised algorithm for training structured predictors that is discriminative, convex, and avoids the use of EM. The idea is to formulate an unsupervised version of structured learning methods, such as maximum margin Markov networks, that can be trained via semidefinite programming. The result is a discriminative training criterion for structured predictors (like hidden Markov models) that remains unsupervised and does not create local minima. To reduce training cost, we reformulate the training procedure to mitigate the dependence on semidefinite programming, and finally propose a heuristic procedure that avoids semidefinite programming entirely. Experimental results show that the convex discriminative procedure can produce better conditional models than conventional Baum-Welch (EM) training.