COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth 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
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Back propagation approach for semi-supervised learning in granular computing
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A transductive multi-label learning approach for video concept detection
Pattern Recognition
Combining active learning and semi-supervised for improving learning performance
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Training pool selection for semi-supervised learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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In many practical machine learning or data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain because labeling the examples require human effort. So, learning with unlabeled data has attracted much attention during the past few years. This paper shows that how such techniques can be helpful in a difficult task, content-based image retrieval, for improving the retrieval performance by exploiting images existing in the database.