Probabilistic Neural Network Training for Semi-Supervised Classifiers

TL;DR

This paper introduces a semi-supervised learning approach that employs a probabilistic neural network to improve classifier performance by effectively utilizing unlabeled data, demonstrated on benchmark datasets.

Contribution

It presents a novel PNN-training algorithm that enhances SVM training with limited labeled data and many unlabeled samples, improving classification accuracy.

Findings

The method outperforms previous semi-supervised techniques on benchmark datasets.

Using PNN for label estimation improves SVM performance.

The approach is effective with few labeled and many unlabeled data.

Abstract

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.