DocumentCode :
3726681
Title :
Evolving Non-Linear Stacking Ensembles for Prediction of Go Player Attributes
Author :
Moudr?k;Roman Neruda
Author_Institution :
Fac. of Math. &
fYear :
2015
Firstpage :
1673
Lastpage :
1680
Abstract :
The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble. This methodology allows for an efficient prediction of different attributes of Go players from sets of their games. These attributes can be fairly general, in this work, we used the strength and style of the players.
Keywords :
"Stacking","Games","Training","Genetic algorithms","Bagging","Biological neural networks","Feature extraction"
Publisher :
ieee
Conference_Titel :
Computational Intelligence, 2015 IEEE Symposium Series on
Print_ISBN :
978-1-4799-7560-0
Type :
conf
DOI :
10.1109/SSCI.2015.235
Filename :
7376811
Link To Document :
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