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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 suggestsdoi:10.1109/icdar.2003.1227819 dblp:conf/icdar/VeeramachaneniN03 fatcat:scax3i7ohveunit3lt247h2uba