Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Text classification based on data partitioning and parameter varying ensembles
Proceedings of the 2005 ACM symposium on Applied computing
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Infinite ensemble learning with support vector machines
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Transductive Support Vector Machine (TSVM) is a method for semi-supervised learning. In order to further improve the classification accuracy and robustness of TSVM, in this paper, we make use of self-training technique to ensemble TSVMs, and classify testing samples by majority voting. The experiment results on 6 UCI datasets show that the classification accuracy and robustness of TSVM could be improved by our approach.