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An Improved Voronoi-Diagram-Based Neural Net for Pattern Classification

Published

Author(s)

Camillo A. Gentile, M Sznaier

Abstract

In this brief paper, we propose a novel two-layer neural network to answer a point query in Rn which is partitioned into polyhedral regions. Such a task solves among others nearest neighbor clustering. As in previous approaches to the problem, our design is based on the use of Voronoi diagrams. However our approach results in substantial reduction of the number of neurons, completely eliminating the second layer, at the price of requiring only two additional clock steps. In addition, the design process is also simplified while retaining the main advantage of the approach, namely its ability to furnish precise values for the number of neurons and the connection weights necessitating neither trial and error type iterations nor ad hoc parameters.
Citation
IEEE Transactions Neural Networks
Volume
12
Issue
5

Keywords

pattern classification, voronoi diagrams

Citation

Gentile, C. and Sznaier, M. (2001), An Improved Voronoi-Diagram-Based Neural Net for Pattern Classification, IEEE Transactions Neural Networks, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151099 (Accessed April 22, 2024)
Created September 12, 2001, Updated February 19, 2017