%0 Conference Paper %1 conf/asc/GestalRDP06 %A Gestal, Marcos %A Rabuñal, Juan R. %A Dorado, Julian %A Pereira Loureiro, Javier %B Artificial Intelligence and Soft Computing %C Palma de Mallorca, Spain %D 2006 %E Pobil, Angel P. Del %I IASTED/ACTA Press %K Algorithm Artificial Capabilities, Example Extraction, Generalisation Generation, Networks, Neural Prediction Recurrent Rule Series algorithms, genetic of programming, %P 323--328 %T Description of RANNs and their generalisation capabilities by means of rule extraction by genetic programming %U http://sabia.tic.udc.es/sabia/secciones/publications/?id=311 %X Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees. %@ 0-88986-612-0

@inproceedings{conf/asc/GestalRDP06, abstract = {Artificial Neural Networks have achieved satisfactory results in different fields such as example classification or image identification. Real-world processes usually have a temporal evolution, and they are the type of processes where Recurrent Networks have special success. Nevertheless they are still reluctantly used, mainly due to the fact that they do not adequately justify their response. But, if ANNs offer good results, why giving them up? Suffice it to find a method that might search an explanation to the outputs that the ANN provides. This work presents a technique, totally independent from ANN architecture and the learning algorithm used, which makes possible the justification of the ANN outputs by means of expression trees.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Palma de Mallorca, Spain}, author = {Gestal, Marcos and Rabu{\~n}al, Juan R. and Dorado, Julian and {Pereira Loureiro}, Javier}, bibdate = {2007-01-26}, bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/asc/asc2006.html#GestalRDP06}, biburl = {https://www.bibsonomy.org/bibtex/211deaf85862c43e3fa3580e36d5f09ec/brazovayeye}, booktitle = {Artificial Intelligence and Soft Computing}, editor = {Pobil, Angel P. Del}, interhash = {331576d28119c1dbefe272c4a9a888f9}, intrahash = {11deaf85862c43e3fa3580e36d5f09ec}, isbn = {0-88986-612-0}, keywords = {Algorithm Artificial Capabilities, Example Extraction, Generalisation Generation, Networks, Neural Prediction Recurrent Rule Series algorithms, genetic of programming,}, month = {August 28-30}, pages = {323--328}, publisher = {IASTED/ACTA Press}, timestamp = {2008-06-19T17:40:11.000+0200}, title = {Description of {RANNs} and their generalisation capabilities by means of rule extraction by genetic programming}, url = {http://sabia.tic.udc.es/sabia/secciones/publications/?id=311}, year = 2006 }