DocumentCode :
3629174
Title :
Building meta-learning algorithms basing on search controlled by machine complexity
Author :
Norbert Jankowski;Krzysztof Grabczewski
Author_Institution :
Department of Informatics at Nicolaus Copernicus University, Toru?, Poland
fYear :
2008
fDate :
6/1/2008 12:00:00 AM
Firstpage :
3601
Lastpage :
3608
Abstract :
Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Meta-learning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored earlier. The learning algorithm is augmented by meta-knowledge repository which accumulates information about progress of the search through the space of candidate solutions. The approach facilitates using human experts knowledge to restrict the search space and provide goal definition, gaining meta-knowledge in an automated manner.
Keywords :
"Complexity theory","Machine learning","Generators","Classification algorithms","Support vector machines","Standardization","Artificial neural networks"
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
ISSN :
2161-4393
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
2161-4407
Type :
conf
DOI :
10.1109/IJCNN.2008.4634313
Filename :
4634313
Link To Document :
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