Designing supervised learning systems is in general a multi-objective optimization problem. It requires finding appropriate trade-offs between several objectives, for example between model complexity and accuracy or sensitivity and specificity. We consider the adaptation of kernel and regularization parameters of support vector machines (SVMs) by means of multi-objective evolutionary optimization. Support vector machines are reviewed from the multi-objective perspective, and different encodings and model selection criteria are described. The optimization of split modified radius-margin model selection criteria is demonstrated on benchmark problems. The MOO approach to SVM design is evaluated on a real-world pattern recognition task, namely the real-time detection of pedestrians in infrared images for driver assistance systems. Here the three objectives are the minimization of the false positive rate, the false negative rate, and the number of support vectors to reduce the computational complexity.
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