DocumentCode :
3649771
Title :
Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation
Author :
O. Kazik;Klara Peková;M. Pilat;R. Neruda
Author_Institution :
Dept. of Theor. Comput. Sci., Charles Univ. Prague, Prague, Czech Republic
Volume :
2
fYear :
2012
Firstpage :
36
Lastpage :
41
Abstract :
The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the experts knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments. In this paper we show the results of the parameter-space search with several search algorithms - tabulation, random search, simmulated annealing, and genetic algorithm.
Keywords :
"Genetic algorithms","Databases","Measurement","Simulated annealing","Training","Error analysis","Data mining"
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Print_ISBN :
978-1-4673-4651-1
Type :
conf
DOI :
10.1109/ICMLA.2012.137
Filename :
6406722
Link To Document :
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