The nature of statistical learning theory
The nature of statistical learning theory
Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
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Facing to the high dimensional data, how to deal them well is the most difficult problem in the field of machine learning, pattern recognition and the relative fields. In this paper, we propose a new semi-supervised multiview learning method, which partition or select the abundant attributes (called attribute partition or attribute selection) into subsets. We consider each subset as a view and on each subset train a classifier to label the unlabeled examples. Based on the ensemble learning, we combine their predictions to classify the unlabeled examples. The semi-supervised learning idea is that to make use of the large number unlabeled example to modify the classifiers iteratively. Experiments on UCI datasets show that this method is feasible and can improve the efficiency. Both theoretical analysis and experiments show that the proposed method has excellent accuracy and speed of classification.