Publication
Title
Gradient descent learning in perceptrons: a review of its possibilities
Author
Abstract
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.
Language
English
Source (journal)
Physical review : E : statistical, nonlinear, and soft matter physics / American Physical Society. - Melville, N.Y., 2001 - 2015
Publication
Melville, N.Y. : American Physical Society, 1995
ISSN
1539-3755 [print]
1550-2376 [online]
Volume/pages
52(1995), p. 1958-1967
ISI
A1995RQ37700082
Full text (Publishers DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
External links
Web of Science
Record
Identification
Creation 08.10.2008
Last edited 22.04.2017