@techreport{2587,
  title = {Global Geometry of SVM Classifiers},
  abstract = {We construct an geometry framework for any norm Support Vector Machine
  (SVM) classifiers. Within this framework, separating hyperplanes, dual descriptions
  and solutions of SVM classifiers are constructed by a purely geometric
  fashion. In contrast with the optimization theory used in SVM classifiers, we have no complicated computations any more. Each step in our
  theory is guided by elegant geometric intuitions.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, T{\"u}bingen, Germany},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2002},
  slug = {2587},
  author = {Zhou, D. and Xiao, B. and Zhou, H. and Dai, R.},
  month_numeric = {6}
}