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 |
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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}, doi = {}, month_numeric = {6} } |