Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Generative-Discriminative Hybrid Method for Multi-View Object Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
The Journal of Machine Learning Research
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
Mining the web for visual concepts
Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008
On semi-supervised learning of Gaussian mixture models for phonetic classification
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Computational Statistics & Data Analysis
Exponential family hybrid semi-supervised learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Semi-supervised learning of visual classifiers from web images and text
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Supervised self-taught learning: actively transferring knowledge from unlabeled data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Help-training semi-supervised LS-SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Language models learning for domain-specific natural language user interaction
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A robust semi-supervised classification method for transfer learning
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Help-Training for semi-supervised support vector machines
Pattern Recognition
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
A geometric view of conjugate priors
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A hybrid generative/discriminative method for semi-supervised classification
Knowledge-Based Systems
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
Machine Vision and Applications
A jointly distributed semi-supervised topic model
Neurocomputing
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We compare two recently proposed frameworks for combining generative and discriminative probabilistic classifiers and apply them to semi-supervised classification. In both cases we explore the tradeoff between maximizing a discriminative likelihood of labeled data and a generative likelihood of labeled and unlabeled data. While prominent semi-supervised learning methods assume low density regions between classes or are subject to generative modeling assumptions, we conjecture that hybrid generative/discriminative methods allow semi-supervised learning in the presence of strongly overlapping classes and reduce the risk of modeling structure in the unlabeled data that is irrelevant for the specific classification task of interest. We apply both hybrid approaches within naively structured Markov random field models and provide a thorough empirical comparison with two well-known semi-supervised learning methods on six text classification tasks. A semi-supervised hybrid generative/discriminative method provides the best accuracy in 75% of the experiments, and the multi-conditional learning hybrid approach achieves the highest overall mean accuracy across all tasks.