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Support Feature Machines (SFM) define useful features derived from similarity to support vectors (kernel transformations), global projections (linear or perceptron-style) and localized projections. Explicit construction of extended feature spaces enables control over selection of features, complexity control and allows final analysis by any classification method. Additionally projections of high-dimensional data may be used to estimate and display confidence of predictions. This approach has been applied to the DNA microarray data.