ML Anthology

Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters

Yuri Kalnishkan
ECML-PKDD 2000 pp. 203-210
doi:10.1007/3-540-45164-1_21 /ecmlpkdd/2000/kalnishkan2000ecml-complexity/

Abstract

In this paper an application of the Complexity Approximation Principle to the non-linear regression is suggested. We combine this principle with the approximation of the complexity of a real-valued vector parameter proposed by Rissanen and thus derive a method for the choice of parameters in the non-linear regression.

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Cite

Text

Kalnishkan. "Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_21

Markdown

[Kalnishkan. "Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/kalnishkan2000ecml-complexity/) doi:10.1007/3-540-45164-1_21

BibTeX

@inproceedings{kalnishkan2000ecml-complexity,
  title     = {{Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters}},
  author    = {Kalnishkan, Yuri},
  booktitle = {European Conference on Machine Learning},
  year      = {2000},
  pages     = {203-210},
  doi       = {10.1007/3-540-45164-1_21},
  url       = {https://mlanthology.org/ecmlpkdd/2000/kalnishkan2000ecml-complexity/}
}