Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2002 (techreport)
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.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems