An introduction to (smoothing spline) ANOVA models in RKHS with examples in geographical data, medicine, atmospheric science and machine learning
Statistics Theory
2007-06-13 v1 Statistics Theory
Abstract
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.
Keywords
Cite
@article{arxiv.math/0410419,
title = {An introduction to (smoothing spline) ANOVA models in RKHS with examples in geographical data, medicine, atmospheric science and machine learning},
author = {Grace Wahba},
journal= {arXiv preprint arXiv:math/0410419},
year = {2007}
}
Comments
This note has appeared in the Proceedings of the 13th IFAC Symposium on System Identification 2003, Rotterdam, 549-559