2026-07-22T11:37:30Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:1383 2024-03-06T11:07:29Z ddc:004 open_access

A general framework for unsupervised preocessing of structured data Hammer, Barbara Micheli, Alessio Sperduti, Alessandro Relational clustering median clustering recursive SOM models kernel SOM 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Barbara Hammer and Alessio Micheli and Alessandro Sperduti 2008 Is Part Of Dagstuhl Seminar Proceedings, Volume 7161, Probabilistic, Logical and Relational Learning - A Further Synthesis (2008) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.07161.2 urn:nbn:de:0030-drops-13837 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07161.2 eng https://creativecommons.org/licenses/by/4.0/legalcode