A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
Low-rank coding with b-matching constraint for semi-supervised classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Sharpened graph ensemble for semi-supervised learning
Intelligent Data Analysis
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This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.