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
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
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
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Deterministic annealing for semi-supervised kernel machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-supervised learning with explicit misclassification modeling
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Simple estimate of the width in Gaussian kernel with adaptive scaling technique
Applied Soft Computing
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Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning principle such as transductive learning has attracted more and more attentions. Transductive support vector machine (TSVM) learns a large margin hyperplane classifier using labeled training data, but simultaneously force this hyperplane to be far away from the unlabeled data. TSVM might seem to be the perfect semi-supervised algorithm since it combines the powerful regularization of SVMs and a direct implementation of the clustering assumption, nevertheless its objective function is non-convex and then it is difficult to be optimized. This paper aims to solve this difficult problem. We apply least square support vector machine to implement TSVM, which can ensure that the objective function is convex and the optimization solution can then be easily found by solving a set of linear equations. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.