Exploiting generative models in discriminative classifiers
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
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 hybrid generative/discriminative approach to text classification with additional information
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bias-Variance Tradeoff in Hybrid Generative-Discriminative Models
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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
Semi-supervised latent variable models for sentence-level sentiment analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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
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We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. Our model is more flexible and natural than previous approaches. Experimental results on several data sets show that our approach also performs better in practice.