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Reinforcement Learning for Board Games: The Temporal Difference Algorithm Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015.
SVM ensembles are better when different kernel types are combined Book Section
In: Lausen, Berthold; Krolak-Schwerdt, Sabine (Hrsg.): Data Science, Learning by Latent Structures, and Knowledge Discovery, S. 191–201, Springer, 2015.
Efficient multi-criteria optimization on noisy machine learning problems Journal Article
In: Applied Soft Computing, Bd. 29, S. 357-370, 2015.
Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, 2015, (Updated version 2015).
The rCMA Tutorial: Examples for using CMA-ES in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, 2015.
Connect-4 Game Playing Framework (C4GPF) Miscellaneous
2014.
Online Adaptable Learning Rates for the Game Connect-4 Technical Report
CIplus Nr. TR 03/2014, 2014, (Preprint version of the article in IEEE Transactions on Computational Intelligence and AI in Games, 2016).
Temporal Difference Learning with Eligibility Traces for the Game Connect-4 Proceedings Article
In: Preuss, Mike; Rudolph, Günther (Hrsg.): CIG'2014, International Conference on Computational Intelligence in Games, Dortmund, S. 84 – 91, 2014.
Reinforcement learning with n-tuples on the game Connect-4 Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.
Tuning and Evolution of Support Vector Kernels Journal Article
In: Evolutionary Intelligence, Bd. 5, S. 153–170, 2012.
