Thema3483: Unterschied zwischen den Versionen
Aus International Center for Computational Logic
Serge Stratan (Diskussion | Beiträge) Keine Bearbeitungszusammenfassung |
Serge Stratan (Diskussion | Beiträge) Keine Bearbeitungszusammenfassung |
||
| Zeile 11: | Zeile 11: | ||
|Abgabe=2006/01/24 | |Abgabe=2006/01/24 | ||
|Ergebnisse=Bachelor mayer-eichberger.pdf | |Ergebnisse=Bachelor mayer-eichberger.pdf | ||
|Beschreibung DE=Combining artificial neural networks and logic programming for machine | |||
learning tasks is the main objective of neural symbolic integration. One | |||
important step towards practical applications in this field is the development | |||
of techniques for extracting symbolic knowledge from neural networks.<br/><br/> | |||
In this thesis a new extraction method is proposed and thoroughly investigated. | |||
It translates the class of feedforward networks with binary threshold | |||
functions into propositional logic programs by means of a decompositional | |||
approach. | |||
|Beschreibung EN=Combining artificial neural networks and logic programming for machine | |||
learning tasks is the main objective of neural symbolic integration. One | |||
important step towards practical applications in this field is the development | |||
of techniques for extracting symbolic knowledge from neural networks.<br/><br/> | |||
In this thesis a new extraction method is proposed and thoroughly investigated. | |||
It translates the class of feedforward networks with binary threshold | |||
functions into propositional logic programs by means of a decompositional | |||
approach. | |||
}} | }} | ||
Aktuelle Version vom 29. November 2016, 22:36 Uhr
Extracting Propositional Logic Programs From Neural Networks: A Decompositional Approach.
Bachelorarbeit, Studienarbeit von Valentin Mayer-Eichberger
- Betreuer Steffen Hölldobler, Sebastian Bader
- Wissensverarbeitung
- 24. Januar 2006 – 24. Januar 2006
- Download
Combining artificial neural networks and logic programming for machine
learning tasks is the main objective of neural symbolic integration. One
important step towards practical applications in this field is the development
of techniques for extracting symbolic knowledge from neural networks.
In this thesis a new extraction method is proposed and thoroughly investigated.
It translates the class of feedforward networks with binary threshold
functions into propositional logic programs by means of a decompositional
approach.
