A learning rule for very simple universal approximators consisting of a single layer of perceptrons
type: Item
instance of: scholarly article
title: A learning rule for very simple universal approximators consisting of a single layer of perceptrons
zbMATH Open document ID: 1254.68196
author: Peter Auer
author: Harald Burgsteiner
author: Wolfgang Maass
published in: Neural Networks
publication date: 2013-01-17T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68Q32
zbMATH DE Number: 6126314
zbMATH Keywords: learning algorithm
zbMATH Keywords: parallel delta rule
zbMATH Keywords: parallel perceptrons
zbMATH Keywords: committee machines
zbMATH Keywords: reduced communication
Wikidata QID: Q34746027
describes a project that uses: UCI-ml
MaRDI profile type: Publication
full work available at URL: j.neunet.2007.12.036
OpenAlex ID: W2043951469
cites work: On the generalization error of fixed combinations of classifiers
cites work: Q4706445
cites work: The Perceptron: A Model for Brain Functioning. I
cites work: Fading memory and the problem of approximating nonlinear operators with Volterra series
cites work: On-line learning in the committee machine
cites work: Large margin classification using the perceptron algorithm
cites work: Q4256326
cites work: Q4936225
cites work: Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
cites work: Q5687947
cites work: Q4000319
cites work: Q5519782
DOI: 10.1016/J.NEUNET.2007.12.036
description: scientific article; zbMATH DE number 6126314