Ruhr-Universität Bochum zum Inhalt Startseite der RUB pix
Startseite UniStartseite
Überblick UniÜberblick
A-Z UniA-Z
Suche UniSuche
Kontakt UniKontakt

pix
 
Das Siegel
Naturwissenschaften Ingenieurwissenschaften Geisteswissenschaften Medizinische Einrichtungen Zentrale Einrichtungen
pix
 
pix Lehrstuhl Mathematik & Informatik
Lower Bounds on Identification Criteria for Perceptron-like Learning Rules
 
 
 
Unser Angebot: Mitarbeiter | Forschung | Lehre   
pix
Startseite » Mitarbeiter » M. Schmitt » Lower Bounds on Identification Criteria for Perceptron-like Learning Rules

pix pix Lower Bounds on Identification Criteria for Perceptron-like Learning Rules
We investigate the computational complexity of identifying neural weights using Perceptron-like learning rules. These are understood as instructions to change weights by a fixed amount after occurrence of an error. We are considering worst-case bounds on the number of correction steps when the training examples are taken from Boolean functions computable by McCulloch-Pitts neurons. In order to avoid known exponential lower bounds for exact identification we define and analyze three criteria that do not require that the learning process generates weights that precisely represent the target function: PAC identification, order identification, and sign identification. Our results show that Perceptron-like learning rules cannot satisfy any of these criteria when the number of correction steps is to be bounded by a polynomial. This indicates that even by considerably lowering one's demands one cannot prevent Perceptron-like rules from being computationally infeasible.

 
 
Zum Seitenanfang  Seitenanfang | Diese Seite drucken
Letzte Änderung: 03.02.2003 | Ansprechpartner: Webmaster