DocumentCode :
3254220
Title :
Arbitrary distance function estimation using vector quantization
Author :
Oommen, B. John ; Altinel, I. Kuban ; Aras, Necati
Author_Institution :
Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada
Volume :
6
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
3062
Abstract :
In this paper we shall utilize the concepts of vector quantization (VQ) for the computation of arbitrary distance functions-a problem which has been receiving much attention in the operations research and location analysis community. The input to our problem is the set of coordinates of a large number of nodes whose inter-node arbitrary “distances” have to be estimated. Unlike traditional operations research methods, which use parametric functional estimators, we have utilized VQ principles to first adaptively polarize the nodes into sub-regions according to Kohonen´s self-organizing map. Subsequently, the parameters characterizing the sub-regions are learnt by using a variety of methods
Keywords :
learning (artificial intelligence); operations research; optimisation; parameter estimation; self-organising feature maps; vector quantisation; Kohonen´s self-organizing map; arbitrary distance function estimation; nodes; operations research; optimisation; parameter estimation; vector quantization; Cities and towns; Computer science; Councils; Lattices; Neural networks; Operations research; Optimization methods; Polarization; Roads; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487272
Filename :
487272
Link To Document :
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