The linear separability problem: some testing methods
Date
2006-03-01
Authors
Advisors
Journal Title
Journal ISSN
ISSN
1045-9227
Volume Title
Publisher
IEEE
Type
Article
Peer reviewed
Yes
Abstract
Description
This article presents an analysis of some of the methods for testing linear separability. A single layer perceptron neural network can be used for creating a classification model when the classes at hand are linearly separable. Since the RDP neural network is based on linearly separable subsets within a non linearly separable set, the performance of the method used for searching these subsets is of great importance in order to minimise convergence time, and maximise the level of generalisation.
It appears in one of the leading journals of Neural Networks with an impact factor of 2.620.
Keywords
RAE 2008, UoA 23 Computer Science and Informatics, class of separability, computational geometry, Fisher linear discriminant, linear programming
Citation
Elizondo, D.A. (2006) The linear separability problem: some testing methods. IEEE Transactions on Neural Networks, 17(2), pp. 330-344.