  Smoothing Spline ANOVA (SS-ANOVA) models in reproducing kernel Hilbert spaces
(RKHS) provide a very general framework for data analysis, modeling and
learning in a variety of fields. Discrete, noisy scattered, direct and indirect
observations can be accommodated with multiple inputs and multiple possibly
correlated outputs and a variety of meaningful structures. The purpose of this
paper is to give a brief overview of the approach and describe and contrast a
series of applications, while noting some recent results.
