Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Training products of experts by minimizing contrastive divergence
Neural Computation
A fast learning algorithm for deep belief nets
Neural Computation
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
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Automatic target recognition (ATR) of objects in side scan sonar imagery typically employs image processing techniques (e.g segmentation, Fourier transform) to extract features describing the objects The features are used to discriminate between sea floor clutter and targets (e.g sea mines) These methods are typically developed for a specific sonar, and are computationally intensive The present work used the Restricted Boltzmann Machine (RBM) to discriminate between images of targets and clutter, achieving a 90% probability of detection and a 15% probability of false alarm, which is comparable to the performance of a Support Vector Machine (SVM) and other state-of-the-art methods on the data The RBM method uses raw image pixels and thus avoids the issue of manually selecting good representations (features) of the data.