New methods for testing linear separability.

Date

2002-08-01

Advisors

Journal Title

Journal ISSN

ISSN

0925-2312

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Description

This paper introduces latest advances in the subject of linear separability. New methods for testing linear separability are introduced. This is a very important area of work which can help simplify the topology of a neural network by using a single layer perceptron when the problem at hand is linearly separable. The research presented in this paper has allowed researchers to enhance the performance of the RDP neural network. It appears in one of the leading journals of Neural Networks.

Keywords

RAE 2008, UoA 23 Computer Science and Informatics, linear separability, convex hull, perceptron, class of separability

Citation

Tajine, M., and Elizondo, D. (2002) New methods for testing linear separability. Neurocomputing, 47(1-4), pp. 161-188.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)