Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The value of agreement, a new boosting algorithm
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Transductive regression piloted by inter-manifold relations
Proceedings of the 24th international conference on Machine learning
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Semi-supervised learning with data calibration for long-term time series forecasting
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online Manifold Regularization: A New Learning Setting and Empirical Study
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Semi-supervised document retrieval
Information Processing and Management: an International Journal
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
DS '09 Proceedings of the 12th International Conference on Discovery Science
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Transductive learning for spatial regression with co-training
Proceedings of the 2010 ACM Symposium on Applied Computing
Combining coregularization and consensus-based self-training for multilingual text categorization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Sparse Semi-supervised Learning Using Conjugate Functions
The Journal of Machine Learning Research
Linear Algorithms for Online Multitask Classification
The Journal of Machine Learning Research
A novel multi-view learning developed from single-view patterns
Pattern Recognition
The Journal of Machine Learning Research
Transductive gaussian process regression with automatic model selection
ECML'06 Proceedings of the 17th European conference on Machine Learning
Double fusion for multimedia event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Web page and image semi-supervised classification with heterogeneous information fusion
Journal of Information Science
Co-regularized ensemble for feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In many applications, unlabelled examples are inexpensive and easy to obtain. Semi-supervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semi-supervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Experiments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.