Location of Repository

A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold

By Simone Fiori

Abstract

Recently we introduced the concept of neural networks learning on Stiefel-Grassman manifold for MLP-like networks. Contributions of other authors have also appeared in the scientific literature about this topic. The aim of this paper is to present a general theory for it, and to illustrate how existing theories may be explained within the general framework proposed here. 1 1 Introduction In a multilayer-perceptron-like network formed by the interconnection of basic neurons, whose only adjustable part consists of weight-vectors, learning the optimal set of connection patterns may be interpreted as selecting the best directions among all possible ones in the space that the weight-vectors belong to (Fyfe, 1995). This interpretation is very useful, in that if a learning error criterion is defined over the weight-space, it measures how much interesting directions are, so that ultimately the rule with which network learns may be conceived as a searching procedure allowing to find out..

Year: 2001
OAI identifier: oai:CiteSeerX.psu:10.1.1.32.8448
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.unipg.it/~sfr/publi... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.