Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Face recognition with semi-supervised learning and multiple classifiers
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Active learning with multiple views
Journal of Artificial Intelligence Research
A co-training approach for time series prediction with missing data
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Maps ensemble for semi-supervised learning of large high dimensional datasets
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Co-training with relevant random subspaces
Neurocomputing
Multiple-view multiple-learner active learning
Pattern Recognition
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
Active learning with extremely sparse labeled examples
Neurocomputing
Combining committee-based semi-supervised learning and active learning
Journal of Computer Science and Technology
A novel multi-view classifier based on Nyström approximation
Expert Systems with Applications: An International Journal
A new co-training-style random forest for computer aided diagnosis
Journal of Intelligent Information Systems
View construction for multi-view semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Multiple-View Multiple-Learner Semi-Supervised Learning
Neural Processing Letters
Using co-training and self-training in semi-supervised multiple classifier systems
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Lateen EM: unsupervised training with multiple objectives, applied to dependency grammar induction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Gene Co-Adaboost: a semi-supervised approach for classifying gene expression data
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
DCPE co-training for classification
Neurocomputing
Information Sciences: an International Journal
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
Disagreement-Based multi-system tracking
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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For many machine learning applications it is important to develop algorithms that use both labeled and unlabeled data. We present democratic co-learning in which multiple algorithms instead of multiple views enable learners to label data for each other. Our technique leverages off the fact that different learning algorithms have different inductive biases and that better predictions can be made by the voted majority. We also present democratic priority sampling, a new example selection method for active learning.