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
Machine Learning
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth 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
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Multirelational classification: a multiple view approach
Knowledge and Information Systems
A relational approach to probabilistic classification in a transductive setting
Engineering Applications of Artificial Intelligence
Adaptive localization in a dynamic WiFi environment through multi-view learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
| Hi-index | 0.00 |
Consider a multi-relational database, to be used for classification, that contains a large number of unlabeled data. It follows that the cost of labeling such data is prohibitive. Transductive learning, which learns from labeled as well as from unlabeled data already known at learning time, is highly suited to address this scenario. In this paper, we construct multi-views from a relational database, by considering different subsets of the tables as contained in a multi-relational database. These views are used to boost the classification of examples in a co-training schema. The automatically generated views allow us to overcome the independence problem that negatively affect the performance of co-training methods. Our experimental evaluation empirically shows that co-training is beneficial in the transductive learning setting when mining multi-relational data and that our approach works well with only a small amount of labeled data.