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Automatic classification of normal mammograms, which constitute a majority of screening mammograms, is a new approach to computer-aided diagnosis of breast cancer. This approach may be limited, however, by non-separable "crossed" distributions of features that are extracted from digitized mammograms. This work presents a method of mapping such non-separable input features into a new set of separable features that can be utilized, together with ordinary "uncrossed" features, by a support vector machine (SVM) classifier. The results of the proposed scheme show improved performance with 80% sensitivity and 95% specificity.