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Abstract: We investigate the learnability, under the uniform distribution, of neural concepts that can be represented as simple combinations of nonoverlapping perceptrons (also called perceptrons) with binary weights and arbitrary thresholds. Two perceptrons are said to be nonoverlapping if they do not share any input variables. Specifically, we investigate, within the distribution-specific PAC model, the learnability of perceptron unions, decision lists , and generalized decision lists . In contrast... (Update)
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BibTeX entry: (Update)
@misc{ golea-learning,
author = "M. Golea and M. Marchand and T. Hancock",
title = "On Learning mu-Perceptron Networks On the Uniform Distribution",
url = "citeseer.ist.psu.edu/golea95learning.html" }
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