Name
Abstract | ||
|---|---|---|
<P>In this article we apply the concepts of vector quantization for the evaluation of arbitrary distance functionsa problem which has important applications in logistics and location analysis. The input to our problem is the set of coordinates of a large number of nodes whose internode arbitrary “distances” have to be estimated. To render the problem interesting, nontrivial and realistic, we assume that the explicit form of this distance function is both unknown and uncomputable. Unlike traditional operations research methods, which compute aggregate parameters of functional estimators according to certain goodness-of-fit criteria, we have utilized vector quantization principles to first adaptively polarize the nodes into subregions. Subsequently, the parameters characterizing the subregions are learned by using a variety of methods (including, for academic purposes a vector quantization strategy in the metadomain). The algorithms have been rigorously tested for the actual roadtravel distances involving cities in Turkey. The results obtained are not only conclusive, but also the best currently available from any single or hybrid strategy.</P> |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1287/ijoc.9.4.439 | INFORMS Journal on Computing |
Keywords | Field | DocType |
neural networks,vector quantization,transportation,travel,kohonen self-organizing map,self,distance function,location | Mathematical optimization,Linde–Buzo–Gray algorithm,Learning vector quantization,Algorithm,Metric (mathematics),Kohonen self organizing map,Vector quantization,Artificial neural network,Quantization (signal processing),Mathematics,Estimator | Journal |
Volume | Issue | Citations |
9 | 4 | 3 |
PageRank | References | Authors |
0.50 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| I. Kuban Altinel | 1 | 150 | 14.60 |
| B. John Oommen | 2 | 1255 | 222.20 |
| Necati Aras | 3 | 462 | 30.62 |