24 August 2000 Pruning method for a cluster-based neural network
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Abstract
Many radar automatic target detection (ATD) algorithms operate on a set of data statistics or features rather than on the raw radar sensor data. These features are selected based on their ability to separate target data samples from background clutter samples. The ATD algorithms often operate on the features through a set of parameters that must be determined from a set of training data that are statistically similar to the data set to be encountered in practice. The designer usually attempts to minimize the number of features used by the algorithm -- a process commonly referred to as pruning. This not only reduces the computational demands of the algorithm, but it also prevents overspecialization to the samples from the training data set. Thus, the algorithm will perform better on a set of test data samples it has not encountered during training. The Optimal Brain Surgeon (OBS) and Divergence Method provide two different approaches to pruning. We apply the two methods to a set of radar data features to determine a new, reduced set of features. We then evaluate the resulting feature sets and discuss the differences between the two methods.
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Kenneth I. Ranney, Kenneth I. Ranney, Hiralal Khatri, Hiralal Khatri, Lam H. Nguyen, Lam H. Nguyen, Jeffrey Sichina, Jeffrey Sichina, } "Pruning method for a cluster-based neural network", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396339; https://doi.org/10.1117/12.396339
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