A robust minimax approach to classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the value of pairwise constraints in classification and consistency
Proceedings of the 24th international conference on Machine learning
Structured large margin machines: sensitive to data distributions
Machine Learning
Discriminatively regularized least-squares classification
Pattern Recognition
Semi-Supervised Learning
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Semi-supervised Gaussian Mixture Model for Image Segmentation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
IEEE Transactions on Neural Networks
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In many real-world classifications such as video surveillance, web retrieval and image segmentation, we often encounter that class information is reflected by the pairwise constraints between data pairs rather than the usual labels for each data, which indicate whether the pairs belong to the same class or not. A common solution is combining the pairs into some new samples labeled by the constraints and then designing a smoothness-driven regularized classifier based on these samples. However, it still utilizes the limited discriminative information involved in the constraints insufficiently. In this paper, we propose a novel semi-supervised discriminatively regularized classifier (SSDRC). By introducing a new discriminative regularization term into the classifier instead of the usual smoothness-driven term, SSDRC can not only use the discriminative information more fully but also explore the local geometry of the new samples further to improve the classification performance. Experiments demonstrate the superiority of our SSDRC.