@article{3e854e7898b6433d86f84adb60128b1f,
title = "A novel measure for quantifying the topology preservation of self-organizing feature maps",
abstract = "Recently, feature maps have been applied to various problem domains. The success of some of these applications critically depends on whether feature maps are topologically ordered. In this paper, we propose a novel measure for quantifying the neighborhood preserving property of feature maps. Two data sets were tested to illustrate the performance of the proposed method.",
keywords = "Feature maps, Neural networks, SOM algorithm, Topological property",
author = "Su, {Mu Chun} and Chang, {Hsiao Te} and Chou, {Chien Hsing}",
note = "Funding Information: This work was partly supported by the National Science Council, Taiwan, R.O.C., under the Grant NSC 89-2614-E-008-001.",
year = "2002",
month = apr,
doi = "10.1023/A:1015240802059",
language = "???core.languages.en_GB???",
volume = "15",
pages = "137--145",
journal = "Neural Processing Letters",
issn = "1370-4621",
number = "2",
}