Bayes classification based on minimum bounding spheres

  title={Bayes classification based on minimum bounding spheres},
  author={Jigang Wang and Predrag Neskovic and Leon N. Cooper},
The minimum bounding sphere of a set of data, defined as the smallest sphere enclosing the data, was first used by Schölkopf et al. to estimate the VC-dimension of support vector classifiers and later applied by Tax and Duin to data domain description. Given a set of data, the minimum bounding sphere of each class can be computed by solving a quadratic programming problem. Since the spheres are constructed for each class separately, they can be used to deal with the multi-class classification… CONTINUE READING