Aj. Smola et B. Scholkopf, ON A KERNEL-BASED METHOD FOR PATTERN-RECOGNITION, REGRESSION, APPROXIMATION, AND OPERATOR INVERSION, Algorithmica, 22(1-2), 1998, pp. 211-231
We present a kernel-based framework for pattern recognition, regressio
n estimation, function approximation, and multiple operator inversion.
Adopting a regularization-theoretic framework, the above are formulat
ed as constrained optimization problems. Previous approaches such as r
idge regression, support vector methods, and regularization networks a
re included as special cases. We show connections between the cost fun
ction and some properties up to now believed to apply to support vecto
r machines only. For appropriately chosen cost functions, the optimal
solution of all the problems described above can be found by solving a
simple quadratic programming problem.