COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Ensembling neural networks: many could be better than all
Artificial Intelligence
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Bootstrapping statistical parsers from small datasets
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Semi-Supervised Kernel Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A hybrid generative/discriminative approach to semi-supervised classifier design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Semi-supervised document retrieval
Information Processing and Management: an International Journal
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
DS '09 Proceedings of the 12th International Conference on Discovery Science
Semi-supervised Classification Based on Clustering Ensembles
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Transductive learning for spatial regression with co-training
Proceedings of the 2010 ACM Symposium on Applied Computing
Combining Local and Global KNN With Cotraining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Software defect detection with rocus
Journal of Computer Science and Technology
Research of immune intrusion detection algorithm based on semi-supervised clustering
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Modeling the temperature of hot rolled steel plate with semi-supervised learning methods
DS'11 Proceedings of the 14th international conference on Discovery science
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Unlabeled data and multiple views
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Interactive genetic algorithms with large population and semi-supervised learning
Applied Soft Computing
CoNet: feature generation for multi-view semi-supervised learning with partially observed views
Proceedings of the 21st ACM international conference on Information and knowledge management
Improving multi-label classification using semi-supervised learning and dimensionality reduction
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning has attracted much attention. Previous research on semi-supervised learning mainly focuses on semi-supervised classification. Although regression is almost as important as classification, semi-supervised regression is largely understudied. In particular, although co-training is a main paradigm in semi-supervised learning, few works has been devoted to co-training style semi-supervised regression algorithms. In this paper, a co-training style semi-supervised regression algorithm, i.e. Coreg, is proposed. This algorithm uses two regressors each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean square error over the labeled neighborhood of that example. Analysis and experiments show that Coreg can effectively exploit unlabeled data to improve regression estimates.