License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.07161.2
URN: urn:nbn:de:0030-drops-13837
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1383/
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Hammer, Barbara ; Micheli, Alessio ; Sperduti, Alessandro

A general framework for unsupervised preocessing of structured data

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07161.HammerBarbara.Paper.1383.pdf (0.2 MB)

Abstract

We propose a general framework for unsupervised recurrent and recursive networks. This proposal covers various popular approaches like standard self organizing maps (SOM), temporal Kohonen maps, resursive SOM, and SOM for structured data. We define Hebbian learning within this general framework. We show how approaches based on an energy function, like neural gas, can be transferred to this abstract framework so that proposals for new learning algorithms emerge.

BibTeX - Entry

@InProceedings{hammer_et_al:DagSemProc.07161.2,
  author =	{Hammer, Barbara and Micheli, Alessio and Sperduti, Alessandro},
  title =	{{A general framework for unsupervised preocessing of structured data}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2008/1383},
  URN =		{urn:nbn:de:0030-drops-13837},
  doi =		{10.4230/DagSemProc.07161.2},
  annote =	{Keywords: Relational clustering, median clustering, recursive SOM models, kernel SOM}
}

Keywords: Relational clustering, median clustering, recursive SOM models, kernel SOM
Collection: 07161 - Probabilistic, Logical and Relational Learning - A Further Synthesis
Issue Date: 2008
Date of publication: 06.03.2008

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