Proximal Knowledge-Based Classification
Abstract
Prior knowledge over general nonlinear sets is incor-
porated into proximal nonlinear kernel classification
problems as linear equalities. The key tool in this
incorporation is the conversion of general nonlinear
prior knowledge implications into linear equalities in
the classification variables without the need to ker-
nelize these implications. These equalities are then
included into a proximal nonlinear kernel classifica-
tion formulation [1] that is solvable as a system of
linear equations. Effectiveness of the proposed formu-
lation is demonstrated on a number of publicly avail-
able classification datasets. Nonlinear kernel classi-
fiers for these datasets exhibit marked improvements
upon the introduction of nonlinear prior knowledge
compared to nonlinear kernel classifiers that do not
utilize such knowledge.
Subject
proximal support vector machines
kernel classification
prior knowledge
Permanent Link
http://digital.library.wisc.edu/1793/64340Citation
06-05