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The problem of classifying sonar signals from rocks and mines first studiedby Gorman and Sejnowski has become a benchmark against which many learningalgorithms have been tested. We show that both the training set and the testset of this benchmark are linearly separable, although with differenthyperplanes. Moreover, the complete set of learning and test patterns together,is also linearly separable. We give the weights that separate these sets, whichmay be used to compare results found by other algorithms.

An optimal linear separator for the Sonar Signals Classification task