On the semi-supervised learning of multi-layered perceptrons
Jonathan Malkin, Amarnag Subramanya, Jeff Bilmes · 2009
We present a novel approach for training a multi-layered perceptron (MLP) in a semi-supervised fashion. Our objective function, when optimized, balances training set accuracy with fidelity to a graph-based manifold over all points. Additionally, the objective favors smoothness via an entropy regularizer over classifier outputs as well as straightforward ℓ2 regularization. Our approach also scales well enough to enable large-scale training. The results demonstrate significant improvement on several phone classification tasks over baseline MLPs. Index Terms: semi-supervised learning, neural networks, phone classification