Preparing for Knowledge Extraction in Modular Neural Networks

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

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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
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Signals and Systems (SAS)
Link to this item:	http://purl.utwente.nl/publications/43136
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