Training TSVM with the proper number of positive samples

The transductive support vector machine (TSVM) is the transductive inference of the support vector machine. The TSVM utilizes the information carried by the unlabeled samples for classification and acquires better classification performance than the regular support vector machine (SVM). As effective as the TSVM is, it still has obvious deficiency: The number of positive samples must be appointed before training and it is not changed during the training phase. This deficiency is caused by the pair-wise exchanging criterion used in the TSVM. In this paper, we propose a new transductive training algorithm by substituting the pair-wise exchanging criterion with the individually judging and changing criterion. Experimental results show that the new method releases the restriction of the appointment of the number of positive samples beforehand and improves the adaptability of the TSVM.