The disputed federalist papers: SVM feature selection via concave minimization

@inproceedings{Fung2003TheDF,
  title={The disputed federalist papers: SVM feature selection via concave minimization},
  author={Glenn Fung},
  booktitle={Richard Tapia Celebration of Diversity in Computing Conference},
  year={2003}
}
In this paper, we use a method proposed by Bradley and Mangasarian "Feature Selection via Concave Minimization and Support Vector Machines" to solve the well-known disputed Federalist Papers classification problem. We find a separating plane that classifies correctly all the "training set" papers of known authorship, based on the relative frequencies of only three words. Using the obtained separating hyperplane in three dimensions, all of the 12 disputed papers ended up on the Madison side of… CONTINUE READING
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