Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Exploitation of Unlabeled Sequences in Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tagging English text with a probabilistic model
Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Semi-supervised conditional random fields for improved sequence segmentation and labeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias-Variance Tradeoff in Hybrid Generative-Discriminative Models
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
Semisupervised Learning of Hidden Markov Models via a Homotopy Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large margin training for hidden Markov models with partially observed states
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On Efficient Large Margin Semisupervised Learning: Method and Theory
The Journal of Machine Learning Research
Homotopy-based semi-supervised Hidden Markov Models for sequence labeling
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Aspects of semi-supervised and active learning in conditional random fields
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Joint Optimization of Hidden Conditional Random Fields and Non Linear Feature Extraction
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Iterative refinement of HMM and HCRF for sequence classification
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
| Hi-index | 0.10 |
Although semi-supervised learning has generated great interest for designing classifiers on static patterns, there has been comparatively fewer works on semi-supervised learning for structured outputs and in particular for sequences. We investigate semi-supervised approaches for learning hidden state conditional random fields for sequence classification. We propose a new approach that iteratively learns a pair of discriminative-generative models, namely Hidden Markov Models (HMMs) and Hidden Conditional Random Fields (HCRFs). Our method builds on simple strategies for semi-supervised learning of HMMs and on strategies for initializing HCRFs from HMMs. We investigate the behavior of the method on artificial data and provide experimental results for two real problems, handwritten character recognition and financial chart pattern recognition. We compare our approach with state of the art semi-supervised methods.