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

fYear

1993

fDate

1993

Firstpage

1207

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298729

Filename

298729

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1906687