Open AccessSelection of number of neurons for vector quantization methods
Author(s) -
Olga Kurasova,
Alma Molytė
Publication year - 2008
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.2008.18121
Subject(s) - vector quantization , learning vector quantization , selection (genetic algorithm) , quantization (signal processing) , artificial intelligence , pattern recognition (psychology) , artificial neural network , set (abstract data type) , computer science , data set , linde–buzo–gray algorithm , machine learning , mathematics , algorithm , programming language
In this paper, a strategy of the selection of the neurons number for vector quantization methods has been investigated. Two methods based on neural networks have been analysed: self-organizing map and neuralgas. There is suggested a way under which the number of neurons is selected taken into account the particularity of the analysed data set.