An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning with progressive transductive support vector machine
Pattern Recognition Letters
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This paper addresses partially supervised classification problems, i.e. problems in which different data sets referring to the same scenario (phenomenon) should be classified but a training information is available only for some of them. In particular, we propose a novel approach to the partially supervised classification which is based on a Bi-transductive Support Vector Machines (T2-SVM). Inspired by recently proposed Transductive SVM (TSVM) and Progressive Transductive SVM (PTSVM) algorithms, the T2-SVM algorithm extracts information from unlabeled samples exploiting the transductive inference, thus obtaining high classification accuracies. After defining the formulation of the proposed T2-SVM technique, we also present a novel accuracy assessment strategy for the validation of the classification performances. The experimental results carried out on a real remote sensing partially supervised problem confirmed the reliability and the effectiveness of both the T2-SVM and the corresponding validation procedure.