An information theoretic approach to adaptive system training using unlabeled data
Traditionally, supervised learning is performed with pairwise input-output labelled data. After the training procedure, the adaptive system weights are fixed and the system is tested with unlabelled data. Recently, exploiting the unlabeled data to improve classification performance has been proposed in the machine learning community. In this paper, we present an information theoretic approach based on density divergence minimization to obtain an extended training algorithm using unlabeled data during testing. The simulations for classification problems suggest that our method can improve the performance of adaptive system in the application phase.