Petra Kudová

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In this paper we present and study a clustering technique based on genetic algorithms Clustering Genetic Algorithm. Performance of the algorithm is demonstrated on experiments. We have shown that it outperforms the k-means algorithm on some tasks. In addition, it is capable of optimising the number of clusters for tasks with well formed and separated(More)
A multiagent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence(More)
In this work we study and develop learning algorithms for networks based on regularization theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a regularization(More)
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