Abstract: The aim of this presentation is to introduce two general schemes used in learning processing. The first one is a generic reinforcement scheme and the second one a scheme for building SVMs kernels. Both schemes are parameters dependent and the improvement of their computational performances is dependent on the choice of these parameters. In the case of the generic reinforcement scheme the performance is measured in number of iterations in learning process and in the case of SVM kernels in the classification accuracy and cross-validation accuracy obtained during many classification tasks. Different kind of genetic algorithms are used for learning parameters optimization.

Brief Biography of the Speaker:
Dana Simian received the diploma. in engineering from the University of Sibiu, Romania, the diploma in Mathematics - Informatics from the University Babes-Bolyai of Cluj-Napoca, Romania and the Ph.D. from Babes-Bolyai University of Cluj- Napoca, Romania. She graduated many courses in Computer Science. She has a great experience in algorithms and numerical methods for modelling and optimization. She published 15 books, more than 60 articles and participated in the editorial board of many scientific publications (proceedings of international conferences and journals).
She organized many special sessions within WSEAS conferences and other international workshops and international conferences on topics related to modeling of intelligent systems, approximation and optimization. She was member of many scientific committees of international conferences. She was involved as director of many research grants.