The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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Proximal support vector machine (PSVM) is proposed instead of SVM, which leads to an extremely fast and simple algorithm by solving a single system of linear equations. However, sometimes the result of PSVM is not accurate especially when the training set is small and inadequate. In this paper, a new PSVM for semi-supervised classification (PS3VM) is introduced to construct the classifier using both the training set and the working set. PS3VM utilizes the additional information of the unlabeled samples from the working set and acquires better classification performance than PSVM when insufficient training information is available. The proposed PS3VM model is no longer a quadratic programming (QP) problem, so a new algorithm has been derived. Our experimental results show that PS3VM yields better performance.