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Title
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Gradient descent learning in perceptrons: a review of its possibilities
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Author
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Abstract
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| We present a streamlined formalism which reduces the calculation of the generalization error for a perceptron, trained on random examples generated by a teacher perceptron, to a matter of simple algebra. The method is valid whenever the student perceptron can be identified as the unique minimum of a specific cost function. The asymptotic generalization error is calculated explicitly for a broad class of cost functions, and a specific cost function is singled out that leads to a generalization error extremely close to the one of the Bayes classifier. |
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Language
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English
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Source (journal)
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Physical review : E : statistical, nonlinear, and soft matter physics / American Physical Society. - Melville, N.Y., 2001 - 2015
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Publication
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Melville, N.Y.
:
American Physical Society
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1995
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ISSN
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1539-3755
[print]
1550-2376
[online]
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DOI
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10.1103/PHYSREVE.52.1958
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Volume/pages
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52
(1995)
, p. 1958-1967
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ISI
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A1995RQ37700082
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Full text (Publisher's DOI)
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Full text (open access)
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