BulDML at Institute of Mathematics and Informatics >
International Book Series Information Science and Computing >
2009 >
Book 08 Classification Forecasting Data Mining >

Please use this identifier to cite or link to this item:

Title: String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors
Authors: Gómez Blas, Nuria
de Mingo, Luis
Gisbert, Francisco
Garitagoitia, Juan
Keywords: Neural Network
Self-Organizing Maps
Control Feedback Methods
Models of Computation
Issue Date: 2009
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: This paper shows some ideas about how to incorporate a string learning stage in self-organizing algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an algorithm is proposed in order to be able to implement a string self-organizing map.
Description: * Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02
ISSN: 1313-0455
Appears in Collections:Book 08 Classification Forecasting Data Mining

Files in This Item:

File Description SizeFormat
ibs-08-p04.pdf201.96 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0!   Creative Commons License