Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Combining active learning and semi-supervised for improving learning performance
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
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Labeled samples are crucial in semi-supervised classification, but which samples should we choose to be the labeled samples? In other words, which samples, if labeled, would provide the most information? We propose a method to solve this problem. First, we give each unlabeled examples an initial class label using unsupervised learning. Then, by maximizing the mutual information, we choose the samples with most information to be user-specified labeled samples. After that, we run semi-supervised algorithm with the user-specified labeled samples to get the final classification. Experimental results on synthetic data show that our algorithm can get a satisfying classification results with active query selection.