  In this paper, we propose another version of help-training approach by
employing a Probabilistic Neural Network (PNN) that improves the performance of
the main discriminative classifier in the semi-supervised strategy. We
introduce the PNN-training algorithm and use it for training the support vector
machine (SVM) with a few numbers of labeled data and a large number of
unlabeled data. We try to find the best labels for unlabeled data and then use
SVM to enhance the classification rate. We test our method on two famous
benchmarks and show the efficiency of our method in comparison with pervious
methods.
