Classification accuracy improvement of neural network classifiers by using unlabeled data

Classification accuracy improvement of neural network classifiers using unlabeled testing data is presented. In order to increase the classification accuracy without increasing the number of training data, the network makes use of testing data along with training data for learning; It is shown that including the unlabeled samples from underrepresented classes in the training set improves the classification accuracy of some of the classes during supervised-unsupervised learning.