ML Anthology

Ridge Regression Learning Algorithm in Dual Variables

Craig Saunders, Alexander Gammerman, Volodya Vovk
ICML 1998 pp. 515-521
/icml/1998/saunders1998icml-ridge/

Abstract

In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by construct-ing a linear regression function in a high di-mensional feature space. The feature space representation can result in a large increase in the number of parameters used by the al-gorithm. In order to combat this “curse of dimensionality”, the algorithm allows the use of kernel functions, as used in Support Vector methods. We also discuss a powerful family of kernel functions which is constructed using the ANOVA decomposition method from the kernel corresponding to splines with an infi-nite number of nodes. This paper introduces a regression estimation algorithm which is a combination of these two elements: the dual version of Ridge Regression is applied to the ANOVA enhancement of the infinite-node splines. Experimental results are then presented (based on the Boston Housing data set) which indicate the performance of this algorithm relative to other algorithms. 1

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Text

Saunders et al. "Ridge Regression Learning Algorithm in Dual Variables." International Conference on Machine Learning, 1998.

Markdown

[Saunders et al. "Ridge Regression Learning Algorithm in Dual Variables." International Conference on Machine Learning, 1998.](https://mlanthology.org/icml/1998/saunders1998icml-ridge/)

BibTeX

@inproceedings{saunders1998icml-ridge,
  title     = {{Ridge Regression Learning Algorithm in Dual Variables}},
  author    = {Saunders, Craig and Gammerman, Alexander and Vovk, Volodya},
  booktitle = {International Conference on Machine Learning},
  year      = {1998},
  pages     = {515-521},
  url       = {https://mlanthology.org/icml/1998/saunders1998icml-ridge/}
}