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Nonparametric regression for learning

1994

Conference Paper

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In recent years, learning theory has been increasingly influenced by the fact that many learning algorithms have at least in part a comprehensive interpretation in terms of well established statistical theories. Furthermore, with little modification, several statistical methods can be directly cast into learning algorithms. One family of such methods stems from nonparametric regression. This paper compares nonparametric learning with the more widely used parametric counterparts and investigates how these two families differ in their properties and their applicability. 

Author(s): Schaal, S.
Book Title: Conference on Adaptive Behavior and Learning
Year: 1994
Publisher: Center of Interdisciplinary Research (ZIF) Bielefeld Germany, also technical report TR-H-098 of the ATR Human Information Processing Research Laboratories

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Cross Ref: p869
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/schaal-ZIF1994.pdf

BibTex

@inproceedings{Schaal_CABL_1994,
  title = {Nonparametric regression for learning},
  author = {Schaal, S.},
  booktitle = {Conference on Adaptive Behavior and Learning},
  publisher = {Center of Interdisciplinary Research (ZIF) Bielefeld Germany, also technical report TR-H-098 of the ATR Human Information Processing Research Laboratories},
  year = {1994},
  note = {clmc},
  crossref = {p869},
  url = {http://www-clmc.usc.edu/publications/S/schaal-ZIF1994.pdf}
}