No abstract

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 clusters.

No abstract

We study approximation problems formulated as regularized minimization problems with kernel-based stabilizers. These approximation schemas exhibit easy derivation of solution to the problem, in the shape of linear combination of kernel functions (one-hidden layer feed-forward neural network schemas). We exploit the article by N. Aronszajn [1] on reproducing kernels and use his formulation of sum of kernels and product of kernels, and resulting kernel space to derive approximation schemas -Sum-Kernel Regularization Network and Product-Kernel Regularization Network. We present some concrete applications of the derived schemas, demonstrate their performance on experiments and compare them to classical solutions.

Three different learning methods for RBF networks and their combinations are presented. Standard gradient learning, three-step algoritm with unsupervised part, and evolutionary algorithm are introduced. Their perfromance is compared on two benchmark problems: Two spirals and Iris plants. The results show that three-step learning is usually the fastest, while gradient learning achieves better precission. The combination of these two approaches gives best results.