Semi-Supervised Learning for Classification with Uncertainty

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 ((SVM)-V-3) has attracted more and more attentions. In general, (SVM)-V-3 deals with problems with small training sets and large working sets. When the training set is large relative to the working set, We propose a new SVM model to solve the above classification problem by introducing the fuzzy memberships to each unlabeled point. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.