License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.07131.7
URN: urn:nbn:de:0030-drops-11332
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2007/1133/
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Schneider, Petra

Relevance Matrices in LVQ

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07131.SchneiderPetra.Paper.1133.pdf (0.4 MB)

Abstract

LVQ-networks belong to the class of distance-based classifiers. The underlying distance measure is of special importance for their performance, because it defines how the data items are compared and how they are grouped in clusters.
Relevance Learning techniques try to adapt the distance measure to the specific data used for training. I will present a new adaptive distance measure in Learning Vector Quantization which is an extension of previously proposed Relevance Learning schemes. In comparison to the already existing techniques for Relevance Learning, this distance measure is more powerful to represent the internal structure of the data appropriately.
Two applications will be used to demonstrate the behavior of the new algorithm (artificial and real life).


BibTeX - Entry

@InProceedings{schneider:DagSemProc.07131.7,
  author =	{Schneider, Petra},
  title =	{{Relevance Matrices in LVQ}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2007/1133},
  URN =		{urn:nbn:de:0030-drops-11332},
  doi =		{10.4230/DagSemProc.07131.7},
  annote =	{Keywords: Learning Vector Quantization, Relevance Learning, adaptive distance measure}
}

Keywords: Learning Vector Quantization, Relevance Learning, adaptive distance measure
Collection: 07131 - Similarity-based Clustering and its Application to Medicine and Biology
Issue Date: 2007
Date of publication: 12.09.2007

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