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|DOI Name=http://dx.doi.org/10.1007/s10489-008-0142-y | |DOI Name=http://dx.doi.org/10.1007/s10489-008-0142-y | ||
|Forschungsgebiet=Neuro-symbolische Integration, | |Forschungsgebiet=Neuro-symbolische Integration, |
Version vom 7. August 2009, 12:54 Uhr
Extracting Reduced Logic Programs from Artificial Neural Networks
Extracting Reduced Logic Programs from Artificial Neural Networks
Veröffentlicht: 2008 September
Journal: Applied Intelligence
Referierte Veröffentlichung
Kurzfassung
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
Download: Media:2008_1895_Lehmann_Extracting Redu_1.pdf,Media:2008_1895_Lehmann_Extracting Redu_2.pdf
DOI Link: http://dx.doi.org/10.1007/s10489-008-0142-y