Abstract
Recently it was pointed out that a well-known benchmark data set, the sonar target data, indeed is linearly separable. This fact comes somewhat surprising, since earlier studies involving delta rule trained perceptrons did not achieve the separation of the training data. These results immediately raise the question of why a perceptron with a continuous activation function may fail to recognize linear separability and how to remedy this failure. The study of these issues directly leads to a performance comparison of a wide variety of different perceptron training procedures on real world data.
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Hasenjäger, M., Ritter, H. Perceptron Learning Revisited: The Sonar Targets Problem. Neural Processing Letters 10, 17–24 (1999). https://doi.org/10.1023/A:1018654611986
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DOI: https://doi.org/10.1023/A:1018654611986