Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-Supervised Kernel Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
A relational approach to probabilistic classification in a transductive setting
Engineering Applications of Artificial Intelligence
Semi-Supervised Learning
Semi-supervised learning by disagreement
Knowledge and Information Systems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Learning and transferring geographically weighted regression trees across time
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
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Many spatial phenomena are characterized by positive autocorrelation, i.e., variables take similar values at pairs of close locations. This property is strongly related to the smoothness assumption made in transductive learning, according to which if points in a high-density region are close, corresponding outputs should also be close. This observation, together with the prior availability of large sets of unlabelled data, which is typical in spatial applications, motivates the investigation of transductive learning for spatial data mining. The task considered in this work is spatial regression. We apply the co-training technique in order to iteratively learn two separate models, such that each model is used to make predictions on unlabeled data for the other. One model is built on the set of attribute-value observations measured at specific sites, while the other is built on the set of aggregated values measured for the same attributes in nearby sites. Experiments prove the effectiveness of the proposed approach on spatial domains.