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- W1976990135 abstract "A neural network learning procedure has been applied to the classification of sonar returns from two undersea targets, a metal cylinder and a similarly shaped rock. Networks with an intermediate layer of hidden processing units achieved a classification accuracy as high as 100% on a training set of 104 returns. These networks correctly classified up to 90.4% of 104 test returns not contained in the training set. This performance was better than that of a nearest neighbor classifier, which was 82.7%, and was close to that of an optimal Bayes classifier. Specific signal features extracted by hidden units in a trained network were identified and related to coding schemes in the pattern of connection strengths between the input and the hidden units. Network performance and classification strategy was comparable to that of trained human listeners." @default.
- W1976990135 created "2016-06-24" @default.
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- W1976990135 date "1988-01-01" @default.
- W1976990135 modified "2023-10-16" @default.
- W1976990135 title "Analysis of hidden units in a layered network trained to classify sonar targets" @default.
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- W1976990135 doi "https://doi.org/10.1016/0893-6080(88)90023-8" @default.
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