A Comparison of Validation Methods for Learning Vector Quantization and for Support Vector Machines on Two Biomedical Data Sets

We compare two comprehensive classification algorithms, support vector machines (SVM) and several variants of learning vector quantization (LVQ), with respect to different validation methods. The generalization ability is estimated by “multiple-hold-out” (MHO) and by “leave-one-out” (LOO) cross v method. The ξα-method, a further estimation method, which is only applicable for SVM and is computationally more efficient, is also used.

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