Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian process classification for segmenting and annotating sequences
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Online Manifold Regularization: A New Learning Setting and Empirical Study
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Robust multi-view boosting with priors
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
CoNet: feature generation for multi-view semi-supervised learning with partially observed views
Proceedings of the 21st ACM international conference on Information and knowledge management
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Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.