Abstract
On-line learning in layered perceptrons is often hampered by plateaus in the time dependence of the performance. Studies on backpropagation in networks with a small number of input units have revealed that correlations between subsequently presented patterns shorten the length of such plateaus. We show how to extend the statistical mechanics framework to quantitatively check the effect of correlations on learning in networks with a large number of input units. The surprisingly compact description we obtain makes it possible to derive properties of on-learning with correlations directly from studies on on-line learning without correlations.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
