A general framework for unsupervised preocessing of structured data

Authors Barbara Hammer, Alessio Micheli, Alessandro Sperduti



PDF
Thumbnail PDF

File

DagSemProc.07161.2.pdf
  • Filesize: 185 kB
  • 6 pages

Document Identifiers

Author Details

Barbara Hammer
Alessio Micheli
Alessandro Sperduti

Cite AsGet BibTex

Barbara Hammer, Alessio Micheli, and Alessandro Sperduti. A general framework for unsupervised preocessing of structured data. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)
https://doi.org/10.4230/DagSemProc.07161.2

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.
Keywords
  • Relational clustering
  • median clustering
  • recursive SOM models
  • kernel SOM

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail