Learning topology of a labeled data set with the supervised generative gaussian graph

Gaillard Pierre, Michael Aupetit, Govaert Gérard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Discovering the topology of a set of labeled data in a Euclidian space can help to design better decision systems. In this work, we propose a supervised generative model based on the Delaunay Graph of some prototypes representing the labeled data.

Original languageEnglish
Title of host publicationESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks
Pages235-240
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
Event15th European Symposium on Artificial Neural Networks, ESANN 2007 - Bruges
Duration: 25 Apr 200727 Apr 2007

Other

Other15th European Symposium on Artificial Neural Networks, ESANN 2007
CityBruges
Period25/4/0727/4/07

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Pierre, G., Aupetit, M., & Gérard, G. (2007). Learning topology of a labeled data set with the supervised generative gaussian graph. In ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks (pp. 235-240)