DocumentCode
3493402
Title
Learning algorithms for a specific configuration of the quantron
Author
de Montigny, S. ; Labib, Richard
Author_Institution
Dept. of Math. & Ind. Eng., Polytech. Montreal, Montreal, QC, Canada
fYear
2011
fDate
July 31 2011-Aug. 5 2011
Firstpage
567
Lastpage
572
Abstract
The quantron is a new artificial neuron model, able to solve nonlinear classification problems, for which an efficient learning algorithm has yet to be developed. Using surrogate potentials, constraints on some parameters and an infinite number of potentials, we obtain analytical expressions involving ceiling functions for the activation function of the quantron. We then show how to retrieve the parameters of a neuron from the images it produced.
Keywords
biology; learning (artificial intelligence); neural nets; pattern classification; artificial neuron model; ceiling functions; learning algorithms; nonlinear classification problems; quantron activation function; surrogate potentials; Algorithm design and analysis; Delay; Equations; Heuristic algorithms; Mathematical model; Neurons; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location
San Jose, CA
ISSN
2161-4393
Print_ISBN
978-1-4244-9635-8
Type
conf
DOI
10.1109/IJCNN.2011.6033271
Filename
6033271
Link To Document