Adaptive Gaussian Pattern Classification
NAVAL OCEAN SYSTEMS CENTER SAN DIEGO CA
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A massively parallel architecture for pattern classification is described. The architecture is based on the field of density estimation. It makes use of a variant of the adaptive kernel estimator to approximate the distributions of the classes as a sum of Gaussian distributions. These Gaussians are learned using a moved mean, moving covariance learning scheme. A temporal ordering scheme is implemented using decay at the input level, allowing the network to learn to recognize sequences. The learning scheme requires a single pass through the data, giving the architecture the capability of real time learning. The first part of the paper develops the adaptive kernel estimator. The parallel architecture is then described, and issues relevant to implementation are discussed. Finally, applications to robotic sensor fusion, intended word recognition, and vision are described. Keywords Gaussian distributions, Density estimation, Pattern recognition.
- Statistics and Probability