In style-constrained classification often there are only a few samples of each style and class, and the correspondences between styles in the training set and the test set are unknown. To avoid gross misestimates of the classifier parameters it is therefore important to model the pattern distributions accurately. We offer empirical evidence for intuitively appealing assumptions, in feature spaces appropriate for symbolic patterns, for (1) tetrahedral configurations of class means that suggests linear style-adaptive classification, (2) improved estimates of classification boundaries by taking into account the asymmetric configuration of the patterns with respect to the directions toward other classes, and (3) pattern-correlated style variability.