ML Anthology

Sequential Bayesian Kernel Regression

Jaco Vermaak, Simon J. Godsill, Arnaud Doucet
NeurIPS 2003 pp. 113-120
/neurips/2003/vermaak2003neurips-sequential/

Abstract

We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is thus applicable to truly sequen- tial data sets and batch data sets alike. The algorithm is based on a generalisation of Importance Sampling, which allows the design of in- tuitively simple and efficient proposal distributions for the model param- eters. Comparative results on two standard data sets show our algorithm to compare favourably with existing batch estimation strategies.

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Text

Vermaak et al. "Sequential Bayesian Kernel Regression." Neural Information Processing Systems, 2003.

Markdown

[Vermaak et al. "Sequential Bayesian Kernel Regression." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/vermaak2003neurips-sequential/)

BibTeX

@inproceedings{vermaak2003neurips-sequential,
  title     = {{Sequential Bayesian Kernel Regression}},
  author    = {Vermaak, Jaco and Godsill, Simon J. and Doucet, Arnaud},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  pages     = {113-120},
  url       = {https://mlanthology.org/neurips/2003/vermaak2003neurips-sequential/}
}