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Global Geometry of SVM Classifiers

2002

Technical Report

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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.

Author(s): Zhou, D. and Xiao, B. and Zhou, H. and Dai, R.
Year: 2002
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
PostScript

BibTex

@techreport{2587,
  title = {Global Geometry of SVM Classifiers},
  author = {Zhou, D. and Xiao, B. and Zhou, H. and Dai, R.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, T{\"u}bingen, Germany},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2002},
  month_numeric = {6}
}