Title
Using large databases and self-organizing maps without tears
Date Issued
01 January 2006
Access level
metadata only access
Resource Type
conference paper
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
Nowadays the need to process lots of complex multimedia databases is more frequent. Recent investigations such as MAM-SOM* and SAM-SOM* families propose the combination of Self-Organizing Maps (SOM) with Access Methods for a faster similarity information retrieval. In this investigation we present experimental results using recent Access Methods such as Slim-Tree and Omni-Sequential that show the improvement acquired by these techniques and their properties in contrast with a traditional SOM network, observing up to 90% of performance improvement. © 2006 IEEE.
Start page
3295
End page
3299
Language
English
OCDE Knowledge area
Ciencias de la computación
Scopus EID
2-s2.0-40649106040
Source
IEEE International Conference on Neural Networks - Conference Proceedings
Resource of which it is part
IEEE International Conference on Neural Networks - Conference Proceedings
ISSN of the container
10987576
ISBN of the container
978-078039490-2
Conference
IEEE International Conference on Neural Networks - Conference Proceedings
Sources of information: Directorio de Producción Científica Scopus