Comparing generatively to discriminatively trained models. We compare the classification performance of classifiers using the MAP principle (solid line) and the MSP principle (dashed line) with the derived prior on differently-sized training data sets for binding sites of the transcription factor Sp1. For both classifiers, we use a PWM model in the foreground and a Markov model of order 3 in the background. We plot the four performance measures, false positive rate, sensitivity, positive predictive value, and area under the precision-recall curve (AUC-PR), against the percentage of the preliminary training data set used for estimating the parameters. Whiskers indicate two-fold standard errors. We find that the classification performance increases with increasing size of the training data set. For the false positive rate this corresponds to a decreasing curve. For all four measures and all sizes of the data set, we find that the discriminatively trained Markov models yield a consistently higher classification performance than the generatively trained Markov models.