Aus Aifbportal
Version vom 15. Juli 2009, 00:17 Uhr von Aifbportal BOT (Diskussion | Beiträge) (Added from ontology)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche

Extracting Reduced Logic Programs from Artificial Neural Networks

Extracting Reduced Logic Programs from Artificial Neural Networks

Veröffentlicht: 2008 September

Journal: Applied Intelligence

Referierte Veröffentlichung


Artificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible-where simple is being understood in some clearly defined and meaningful way.

ISSN: 1573-7497
Weitere Informationen unter: Link
DOI Link:




Neuro-symbolische Integration