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This paper expands our previous activities on automated texture analysis applying optimized nonlinear and oriented kernels. The operator parameterization is achieved using particle swarm optimization (PSO). The sensitivity of the voting k-nearest-neighbor (kNN) classifier used in the optimization process and for texture classification is explored in respect of the number of used neighbors. Additionally, support vector machines (SVM) with the reputation to procure better results are applied. Contrary to a recommended grid search for the parameter selection, the adaptation of the free SVM parameters is included into the global optimization process with PSO. Our work was tested with benchmark and application data from leather inspection.