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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15917
Title: Introduction to the SAM-S M* and MAM-S M* families
Authors: Cuadros Vargas, Ernesto
Romero, Francelin
Keywords: Algorithms;Distributed computer systems;Functions;Information retrieval;Mathematical models;Pattern recognition;Metric Access Method (MAM);Spatial Access Method (SAM);Training algorithms;Self organizing maps
Issue Date: 2005
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750136102&doi=10.1109%2fIJCNN.2005.1556397&partnerID=40&md5=a325897b605ae794240d26556f76fa9c
Abstract: In this paper, two new families of constructive Self-Organizing Maps (SOMs), SAM-SOM* and MAM-SOM*, are proposed. These families are specially useful for information retrieval from large databases, high-dimensional spaces and complex distance functions which usually consume a long time. They are generated by incorporating Spatial Access Method (SAM) and Metric Access Method (MAM) into SOM with the maximum insertion rate, i.e. the case when a new unit is created for each pattern presented to the network. In this specific case, the network presents interesting advantages and acquires new properties which are quite different of traditional SOM. In a constructive SOM, if new units are rarely inserted into network, the training algorithm would probably need a long time to converge. On the other hand, if new units are inserted frequently, the training algorithm would not have enough time to adapt these units to the data distribution. Besides, training time is increased because the search for the winning neuron is traditionally performed sequentially. The use of SAM and MAM combined with SOM open the possibility of training constructive SOM with as much units as existing patterns with less time and interesting advantages compared with both models: Kohonen network SOM and SAM-SOM model (SOM using SAM). Advantages and drawbacks of these new families are also discussed. These new families are useful to improve both SOM and SAM techniques.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15917
ISBN: urn:isbn:9780780390485
Appears in Collections:Artículos - Ciencia de la computación

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