Preparing for Knowledge Extraction in Modular Neural Networks


Spaanenburg, Lambert and Slump, Cees and Venema, Rienk and Zwaag, Berend-Jan van der (2002) Preparing for Knowledge Extraction in Modular Neural Networks. In: 3rd IEEE Benelux Signal Processing Symposium, SPS, March 21-22, 2002, Leuven, Belgium (pp. pp. 121-124).

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Abstract:Neural networks learn knowledge from data. For a monolithic
structure, this knowledge can be easily used but not isolated. The
many degrees of freedom while learning make knowledge
extraction a computationally intensive process as the
representation is not unique. Where existing knowledge is
inserted to initialize the network for training, the effect becomes
subsequently randomized within the solution space. The paper
describes structuring techniques such as modularity and hierarchy
to create a topology that provides a better view on the learned
knowledge to support a later rule extraction.
Item Type:Conference or Workshop Item
Additional information:SAS 023N02
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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