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The classification of complex data usually requires the composition ofprocessing steps. Here, a major challenge is the selection of optimalalgorithms for preprocessing and classification (including parameterizations).Nowadays, parts of the optimization process are automized but expert knowledgeand manual work are still required. We present three steps to face this processand ease the optimization. Namely, we take a theoretical view on classicalclassifiers, provide an approach to interpret the classifier together with thepreprocessing, and integrate both into one framework which enables asemiautomatic optimization of the processing chain and which interfacesnumerous algorithms.

Generalizing, Decoding, and Optimizing Support Vector Machine Classification