In this talk we utilize a support vector machine feature selection procedure via concave minimization to solve the well-known Disputed Federalist Papers classification problem. First we find a separating plane that classifies correctly all the training set consisting of papers of known authorship, based on the relative frequencies of three words only. Then, using this three-dimensional separating plane, all of the 12 disputed papers ended up on one side of the separating plane. Our result coincides with previous statistical and combinatorial method results.