Nonlinear Knowledge in Kernel Approximation
Abstract
Prior knowledge over arbitrary general sets is
incorporated into nonlinear kernel approximation problems in
the form of linear constraints in a linear program. The key
tool in this incorporation is a theorem of the alternative for
convex functions that converts nonlinear prior knowledge implications
into linear inequalities without the need to kernelize
these implications. Effectiveness of the proposed formulation is
demonstrated on two synthetic examples and an important lymph
node metastasis prediction problem. All these problems exhibit
marked improvements upon the introduction of prior knowledge
over nonlinear kernel approximation approaches that do not
utilize such knowledge.
Subject
nonlinear kernel approximation
Permanent Link
http://digital.library.wisc.edu/1793/64332Citation
05-05