#Mozer, M., and P. Smolensky, A technique for trimming the fat from a network via relevance assessment, in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 598-605 (1989). Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Evolving Fuzzy Neural Networks for Supervised/Unsupervised.. - Kasabov (2001) (1 citation) (Correct)
.... incremental learning [6,7,8,9,19,53,58,61,71] lifelong learning [69,35,36,82] on line learning [17,21,22,28,31,35,36,42,44,61,66,67,69] constructivist structural learning [15,19,11,14,9] that is supported by biological facts [14,62,73,77,82] selectivist structural learning [26,29,49,56,59,64,50,32]; hybrid constructivist selectivist structural learning December,2001 3 [52,66,70,31] knowledge based learning neural networks (KBNN) 57,24,25,30,33,38,44, 45,51,63,76,77,83] The EFuNN model presented in the paper has elements from all the groups above. The model is called evolving because ....
Mozer, M., and P. Smolensky, "A technique for trimming the fat from a network via relevance assessment", in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 598-605 (1989).
On-Line Learning, Reasoning, Rule Extraction and Aggregation in.. - Kasabov (2001) (2 citations) (Correct)
....that is adequate to the expected accuracy of the system. Reducing the structure of a KBNN can be achieved through regular pruning of nodes and connections thus allowing for knowledge to emerge in the structure, or through aggregating nodes into bigger rule clusters. The former approach is used in [19,21,38,39,41,44,46,47,53]. The latter one is explored in this paper. It is based on a regular aggregation of rule nodes in the KBNN structure, which is equivalent to aggregating rules into rule clusters before new data and new knowledge is accommodated in the system. This is the case with the EFuNNs. Different KBNNs are ....
M. Mozer, P. Smolensky, A technique for trimming the fat from a network via relevance assessment, in: D. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufmann, Los Altos, CA, 1998, pp. 598-605.
Evolving Fuzzy Neural Networks for Supervised/Unsupervised.. - Kasabov (2001) (1 citation) (Correct)
.... [6,7,8,9,19,53,58,61,71] learning [4,5,7,8,14,30,46,47,48] incremental lifelong learning [69,35,36,82] on line [17,21,22,28,31,35,36,42,44,61,66,67,69] constructivist structural [ 15,19,11,14,9] that is supported by biological facts [ 14,62,73,77, 82] selectivist structural learning [26,29,49,56,59,64,50,32]; hybrid constructivist selectivist structural learning 2 [52,66,70,31] knowledge based learning neural networks (KBNN) 57,24,25,30,33,38,44, 45,51,63,76,77,83] The EFuNN model presented in the paper has elements from all the groups above. The model is called evolving because of the nature of ....
Mozer, M., and P. Smolensky, "A technique for trimming the fat from a network via relevance assessment", in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol. 2, Morgan Kaufmann, 598-605 (1989).
On-Line Learning, Reasoning, Rule Extraction and Aggregation in.. - Kasabov (2001) (2 citations) (Correct)
....that is adequate to the expected accuracy of the system. Reducing the structure of a KBNN can be achieved through regular pruning of nodes and connections thus allowing for knowledge to emerge in the structure, or through aggregating nodes into bigger rule clusters. The former approach is used in [19,21,38,39,41,44,46,47,53]. The latter one is explored in this paper. It is based on a regular aggregation of rule nodes in the KBNN structure, which is equivalent to aggregating rules into rule clusters before new data and new knowledge is accommodated in the system. This is the case with the EFuNNs. Di erent KBNNs are ....
M. Mozer, P. Smolensky, A technique for trimming the fat from a network via relevance assessment, in: D. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufmann, Los Altos, CA, 1998, pp. 598}605.
Dynamic Evolving Fuzzy Neural Networks with `m-out-of-n'.. - Kasabov, Song (1999) (5 citations) (Correct)
No context found.
#Mozer, M., and P. Smolensky, A technique for trimming the fat from a network via relevance assessment, in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 598-605 (1989).
DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its .. - Kasabov, Song (2001) (Correct)
No context found.
Mozer, M., and P. Smolensky, "A technique for trimming the fat from a network via relevance assessment", in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 598-605 (1989). 30
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