Document (#24663)

Author

Czap, H.

Title

Theorienbildung mittels Künstlich Neuronaler Netze vom Typ MLP

Source

Globalisierung und Wissensorganisation: Neue Aspekte für Wissen, Wissenschaft und Informationssysteme: Proceedings der 6. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Hamburg, 23.-25.9.1999. Hrsg.: H.P. Ohly, G. Rahmstorf u. A. Sigel

Imprint

Würzburg : Ergon Verlag

Year

2000

Pages

S.391-398

Series

Fortschritte in der Wissensorganisation; Bd.6

Abstract

Künstlich Neuronale Netze (KNN) vom Typ Multi-Layer-Perceptron (MILP) erlauben nach herrschender Literaturmeinung nicht, den Knoten der verborgenen Schicht eine spezifische Semantik zuzuordnen. Dieser Ansicht wurde vom Autor in [Czap 96] widersprochen. Danach kann die Clusterbildung der ersten verborgenen Schicht eines nachfolgend fehlerfreien Netzes einer spezifischen Interpretation zugänglich sein. An dem "Two-or-more-Clumps"-Problem, einem numerischen Beispiel, wird in Grundzügen ein Konstruktionsverfahren zur Bildung von KNNs angegeben, deren erste verborgene Schicht interpretiert werden kann, d.h. inhaltliche Relevanz besitzt. Im Sinne eines "Knowledge Discovery" wird an diesem Beispiel illustriert, dass KNN in der Lage sind, die in der Input-Output-Beziehung eines vorliegenden Datensamples implizit vorhandene Gesetzmäßigkeit zu erkennen und in eine dem menschlichen Verständnis zugängliche Repräsentation zu überführen. Zentraler Ansatzpunkt zur Ableitung der die Input-Output-Beziehung verursachenden Gesetzmäßigkeit ist die Bildung von Klassifizierungen im Merkmalsraum, wobei die entstehenden Cluster unmittelbar interpretierbar sind. Als eine zentrale Aufgabe von Wissensordnungen wird häufig die Unterteilung vorliegender Phänomene in geeignete Klassen verstanden. Dieser Beitrag zeigt, dass Klassenbildungen, die nach rein numerischen Gesichtspunkten mittels eines KNN erfolgen, unter gewissen Bedingungen - entscheidend sind Minimalität sowie nachfolgende Fehlerfreiheit des Netzes - durch den Menschen interpretierbar sind und somit einen wichtigen Beitrag zu einer Theorienbildung leisten können, auch wenn die Gesetzmäßigkeit, die die Input-Output-Beziehung verursacht, nicht bekannt ist. Vermutet wird, dass beliebige Verfahren zur Erstellung von KNN immer dann zu Clusterungen der Merkmalssätze im Input-Raum fuhren, die inhaltliche Aussagen gestatten, wenn die Anzahl der entstehenden Cluster minimal ist und mögliche Abbildungsfehler ausschließlich durch die Clusterbildung bedingt sind

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