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Generation of explicit knowledge from empirical data through pruning of trainable neural networks

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journal contribution
posted on 06.06.2018, 09:08 by Alexander N. Gorban, Eugeniy M. Mirkes, Victor G. Tsaregorodtsev
This paper presents a generalized technology of extraction of explicit knowledge from data. The main ideas are 1) maximal reduction of network complexity (not only removal of neurons or synapses, but removal all the unnecessary elements and signals and reduction of the complexity of elements), 2) using of adjustable and flexible pruning process (the pruning sequence shouldn't be predetermined - the user should have a possibility to prune network on his own way in order to achieve a desired network structure for the purpose of extraction of rules of desired type and form), and 3) extraction of rules not in predetermined but any desired form. Some considerations and notes about network architecture and training process and applicability of currently developed pruning techniques and rule extraction algorithms are discussed. This technology, being developed by us for more than 10 years, allowed us to create dozens of knowledge-based expert systems.

History

Citation

Proceedings of the International Joint Conference on Neural Networks IJCNN '99, 1999, 6, pp. 4393-4398

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Mathematics

Source

International Joint Conference on Neural Networks IJCNN '99, Washington, DC, USA

Version

AM (Accepted Manuscript)

Published in

Proceedings of the International Joint Conference on Neural Networks IJCNN '99

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1098-7576

isbn

0-7803-5529-6

Copyright date

2002

Available date

06/06/2018

Publisher version

https://ieeexplore.ieee.org/document/830876/

Temporal coverage: start date

10/07/1999

Temporal coverage: end date

16/07/1999

Language

en

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