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Support Vector Machine (SVM) classification requires set of one or more parameters and these parameters have significant influence on classification precision and generalization ability. Searching for suitable model parameters invokes great computational load, which accentuates with increasing size of the dataset and with amount of the parameters being optimized. In this paper we present and compare various SVM parameters selection techniques, namely grid search, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Experiments conducting over two datasets show promising results with PSO and GA optimization technique.