Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Machine Learning - Special issue on learning with probabilistic representations
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An entropic estimator for structure discovery
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Eighteenth national conference on Artificial intelligence
Connected Vibrations: A Modal Analysis Approach for Non-Rigid Motion Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Emotion Recognition Using a Cauchy Naive Bayes Classifier
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
The relative value of labeled and unlabeled samples in pattern recognition
The relative value of labeled and unlabeled samples in pattern recognition
Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Semisupervised learning of classifiers with application to human-computer interaction
Semisupervised learning of classifiers with application to human-computer interaction
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Emotion recognition using facial expressions with active appearance models
HCI '08 Proceedings of the Third IASTED International Conference on Human Computer Interaction
Semi-supervised learning of dynamic self-organising maps
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
Enhancing expression recognition in the wild with unlabeled reference data
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Automatic classification by machines is one of the basic tasks required in any pattern recognition and human computer interaction applications. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in a facial expression recognition application.