Abstract
After decades of concurrent development of symbolic and connectionist methods, recent years have shown intensifying efforts of integrating those two paradigms. This paper contributes to the development of methods for transferring present symbolic knowledge into connectionist representations. Motivated by basic ideas from formal concept analysis, we propose two ways of directly encoding closure operators on finite sets in a 3-layered feed forward neural network.
This is an extended version of a paper presented at NeSy07 – the Third Workshop on Neural-Symbolic-Integration at IJCAI 2007. Sebastian Rudolph is supported by the Deutsche Forschungsgemeinschaft (DFG) under the ReaSem project and by the European Union under the NeOn project (IST-2005-027595).
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Rudolph, S. (2007). Using FCA for Encoding Closure Operators into Neural Networks. In: Priss, U., Polovina, S., Hill, R. (eds) Conceptual Structures: Knowledge Architectures for Smart Applications. ICCS 2007. Lecture Notes in Computer Science(), vol 4604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73681-3_24
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DOI: https://doi.org/10.1007/978-3-540-73681-3_24
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