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Hybrid neural network classifiers for automatic target detection
Author(s) -
Katz Alan,
Thrift Philip
Publication year - 1993
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.1993.tb00104.x
Subject(s) - computer science , artificial intelligence , backpropagation , classifier (uml) , artificial neural network , estimator , pattern recognition (psychology) , machine learning , infomax , mathematics , statistics , computer network , channel (broadcasting) , blind signal separation
We describe a one‐class classification approach to an automatic target detection problem, which involves distinguishing targets from clutter in diverse environments. We use only target statistics to construct the classifier. The classifier combines conventional and neural network methods. The classifier is a Parzen estimator, which requires storage and recall of all training points. To reduce the size of the training set, we apply two neural network learning algorithms: (1) we use a backpropagation network to approximate the Parzen estimator; (2) we apply the infomax learning principle to compress the size of the training set before constructing the Parzen estimator. We find that the results obtained with the infomax scheme approach those obtained with Parzen alone and are better than those obtained with backpropagation.