Probabilistic Neural Network Training for Semi-Supervised Classifiers
This is an incremental improvement for semi-supervised learning tasks.
The paper tackles the problem of semi-supervised classification with limited labeled data by proposing a Probabilistic Neural Network (PNN) training algorithm to improve SVM performance, showing efficiency on two benchmarks.
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.