Publication
Title
Learning by examples from a non-uniform distribution
Author
Abstract
We present a general replica calculation for learning from examples generated by a nonuniform pattern distribution with a single symmetry-breaking orientation. Our results cover the three main learning scenarios: storage of patterns with random classifications by a perceptron, supervised learning from a teacher, and unsupervised learning. We show that for a perceptron the critical storage capacity αc=2 is completely independent of the pattern distribution provided it is point symmetric or provided the classification as ± 1 is unbiased. In a particular model for supervised learning we find that an ideal (Bayes) student learns most from a few examples if they are easy and from a large number if they are difficult. Learning based on the minimization of a specific class of (quadratic) cost functions is solved completely for all three scenarios.
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, 1996
ISSN
1539-3755 [print]
1550-2376 [online]
Volume/pages
53(1996), p. 3989-3998
ISI
A1996UH48200042
Full text (Publisher's 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 26.06.2017