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In information processing systems for classification and regression tasks, global parameters are often introduced to balance the prior expectation about the processed data and the emphasis on reproducing the training data. Since over-emphasizing either of them leads to poor generalization, optimal global parameters are needed. Conventionally, a time-consuming cross-validation procedure is used. Here we introduce a novel approach to this problem, based on the Green's function. All estimations can be made empirically and hence can be easily extended to more complex systems. The method is fast since it does not require the validation step. Its performances on benchmark data sets are very satisfactory. [ABSTRACT FROM AUTHOR]