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This work presents an approach based on the kernelized discriminant analysis to classify symbolic interval data in nonlinearly separable problems. It is known that the use of kernels allows to map implicitly data into a high-dimensional space, called feature space; computing projections in this feature space results in a nonlinear separation in the input space that is equivalent to linear separating...