Titlebook: Artificial Intelligence and Soft Computing – ICAISC 2008; 9th International Co Leszek Rutkowski,Ryszard Tadeusiewicz,Jacek M. Zur Conferenc

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Facilitating the Genetic Counseling Processotor flux reference frame. Two approaches are considered: data mining with GMDH algorithm and gradual training of the NN in the desired frequency range. In both cases the accuracy of the estimator is significantly improved. Provided tests confirmed this feature and encourage to implement such an est

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Listening to Clients: Attending Skills,ets, which contain the data from clinical studies following patients response for a given treatment. Such datasets may contain incomplete (censored) information on patients failure times. The proposed method is able to cope with censored observations and as the result returns the aggregated Kaplan-M

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Facilitating the Genetic Counseling Processsk minimization and complexity regularization. We study convergence of the RBF networks for various radial kernels as the number of training samples increases. The rates of convergence are also examined.

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Facilitating the Genetic Counseling Processroblem) and a steady-state one (for the economic optimisation subproblem). The algorithm is computationally efficient because it needs solving on-line only one quadratic programming problem. Unlike the classical control system structure, the necessity of repeating two nonlinear optimisation problems

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Facilitating the Genetic Counseling Processormance. In this paper a new, robust to outliers learning algorithm, employing the concept of initial data analysis by the MCD (minimum covariance determinant) estimator, is proposed. Results of implementation and simulation of nets trained with the new algorithm and the traditional backpropagation