-
[show abstract]
[hide abstract]
ABSTRACT: Slow feature analysis (SFA) is a bioinspired method for extracting slowly varying driving forces from quickly varying non-stationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g., the envelope of a modulated sine wave). It depends on circumstances like the embedding dimension, the time series predictability, or the base frequency, whether the driving force itself or a slower subcomponent is detected. Interestingly, we observe a swift phase transition from one regime to another and it is the objective of this work to quantify the influence of various parameters on this phase transition. We conclude that what is perceived as slow by SFA varies and that a more or less fast switching from one regime to another occurs, perhaps showing some similarity to human perception. Reference to this paper should be made as follows: Konen, W. and Koch, P. (2011) 'The slowness principle: SFA can detect different slow components in non-stationary time series', Int.
Int. J. Innovative Computing and Applications J. Innovative Computing and Applications. ; 3(3):3-10.
-
[show abstract]
[hide abstract]
ABSTRACT: Computational Intelligence (CI) provides good and robust working solutions for global optimization. CI is especially suited for solv-ing difficult tasks in parameter optimization when the fitness function is noisy. Such situations and fitness landscapes frequently arise in real-world applications like Data Mining (DM). Unfortunately, parameter tuning in DM is computationally expensive and CI-based methods often require lots of function evaluations until they finally converge in good solutions. Earlier studies have shown that surrogate models can lead to a decrease of real function evaluations. However, each function evaluation remains time-consuming. In this paper we investigate if and how the fitness land-scape of the parameter space changes, when only fewer observations are used for the model trainings during tuning. A representative study on seven DM tasks shows that the results are nevertheless competitive. On all these tasks, a fraction of 10-15% of the training data is sufficient. With this the computation time can be reduced by a factor of 6-10.
PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina; 09/2012
-
[show abstract]
[hide abstract]
ABSTRACT: Learning complex game functions is still a difficult task. We apply temporal difference learning (TDL), a well-known variant of the reinforcement learning approach, in combination with n-tuple networks to the game Connect-4. Our agent is trained just by self-play. It is able, for the first time, to consistently beat the optimal-playing Minimax agent (in game situations where a win is possible). The n-tuple network induces a mighty feature space: It is not necessary to design certain features, but the agent learns to select the right ones. We believe that the n-tuple network is an important ingredient for the overall success and identify several aspects that are relevant for achieving high-quality results. The architecture is sufficiently general to be applied to similar reinforcement learning tasks as well.
PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina; 09/2012
-
[show abstract]
[hide abstract]
ABSTRACT: Kernel-based methods like Support Vector Machines (SVM) have been established as powerful techniques in machine learning. The idea of SVM is to perform a mapping from the input space to a higher-dimensional feature space using a kernel function, so that a linear learning algorithm can be employed. However, the burden of choosing the appropriate kernel function is usually left to the user. It can easily be shown that the accuracy of the learned model highly depends on the chosen kernel function and its parameters, especially for complex tasks. In order to obtain a good classification or regression model, an appropriate kernel function in combination with optimized pre- and post-processed data must be used. To circumvent these obstacles, we present two solutions for optimizing kernel functions: (a) automated hyperparameter tuning of kernel functions combined with an optimization of pre- and post-processing options by Sequential Parameter Optimization (SPO) and (b) evolving new kernel functions by Genetic Programming (GP). We review modern techniques for both approaches, comparing their different strengths and weaknesses. We apply tuning to SVM kernels for both regression and classification. Automatic hyperparameter tuning of standard kernels and pre- and post-processing options always yielded to systems with excellent prediction accuracy on the considered problems. Especially SPO-tuned kernels lead to much better results than all other tested tuning approaches. Regarding GP-based kernel evolution, our method rediscovered multiple standard kernels, but no significant improvements over standard kernels were obtained.
Evolutionary Intelligence 01/2012; 5:153-170.
-
06/2011;
-
Proceedings 21. Workshop Computational Intelligence; 01/2011
-
International Journal of Innovative Computing and Applications (IJICA). 01/2011; 3(1):3-10.
-
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, New York, NY, USA; 01/2011
-
CIOP Reports. 01/2011;
-
01/2011;
-
Proceedings 21. Workshop Computational Intelligence; 01/2011
-
13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
-
01/2011;
-
02/2010;
-
01/2010;
-
Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010; 01/2010
-
Proceedings 20. Workshop Computational Intelligence; 01/2010
-
Proc. 2010 Congress on Evolutionary Computation (CEC'10) within IEEE World Congress on Computational Intelligence (WCCI'10), Barcelona, Spain, Piscataway NJ; 01/2010
-
Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18-23 July 2010; 01/2010
-
Proceedings 20. Workshop Computational Intelligence; 01/2010
