Title :
Is LVQ really good for classification?-an interesting alternative
Author :
Poechmueller, W. ; Glesner, M. ; Juergs, H.
Author_Institution :
Inst. for Microelectron. Syst., Darmstadt Inst. of Technol., Germany
Abstract :
Learning vector quantization (LVQ), developed by T. Kohonen (1989), is a neural network based method to find a good set of reference vectors to be stored as a nearest neighbor classifier´s reference set. An efficient method of finding reference vectors along class boundaries instead of finding vectors representing class distribution, as LQV does, is described. A quantitative comparison with LVQ is given
Keywords :
learning (artificial intelligence); neural nets; vector quantisation; class boundaries; learning vector quantization; nearest neighbor classifier´s reference set; neural network based method; reference vectors; Benchmark testing; Classification algorithms; Density functional theory; Lapping; Microelectronics; Nearest neighbor searches; Neural networks; Probability density function; Satellites; Vector quantization;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298729
