Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Semi-supervised protein classification using cluster kernels
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
SemiBoost: Boosting for Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Labeling Strategy for Semi-supervised Document Categorization
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Simultaneous clustering and classification over cluster structure representation
Pattern Recognition
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
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Semi-Supervised Support Vector Machines (S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supervised) SVM. In this paper, we show that S3VMs, with knowledge of the means of the class labels of the unlabeled data, is closely related to the supervised SVM with known labels on all the unlabeled data. This motivates us to first estimate the label means of the unlabeled data. Two versions of the meanS3VM, which work by maximizing the margin between the label means, are proposed. The first one is based on multiple kernel learning, while the second one is based on alternating optimization. Experiments show that both of the proposed algorithms achieve highly competitive and sometimes even the best performance as compared to the state-of-the-art semi-supervised learners. Moreover, they are more efficient than existing S3VMs.