Supervised Training of Adaptive Systems with Partially Labeled Data

Abstract

Supervised adaptive system training is traditionally performed with available pairs of input-output data and the system weights are fixed following this training procedure. Recently, in the context of machine learning, where the desired outputs are discrete-valued, the idea of exploiting unlabeled samples for improving classification performance has been proposed. We introduce an information theoretic framework based on density divergence minimization to obtain extended training algorithms. Our goal is to provide a theoretical framework upon which we can build efficient algorithms to this end.