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A support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose genetic syndromes. The study implements the SVM structural risk minimization concept in searching for the optimal setting of the classifier kernel and parameters. We propose thresholding the distance of tested patterns from the SVM separating hyperplane as a way of rejecting a percentage of the misclassified patterns, thereby allowing reduction of the expected risk. Results show accurate performance of the SVM in classifying FISH signals in comparison to other state-of-the-art machine learning classifiers, indicating the potential of an SVM-based genetic diagnosis system.