We study two different unsupervised learning strategies for a single-layer perceptron. The environment provides a set of unclassified training examples, which belong to two different classes, depending on their overlap with an N-dimensional concept vector. By means of a statistical-mechanics analysis, using the replica method, we investigate how well the perceptron infers the unknown structure from the input data.
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Biehl et al. (1993) studied this question.
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