Principles of Risk Minimization for Learning Theory
Abstract
Learning is posed as a problem of function estimation, for which two princi(cid:173) ples of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different state(cid:173) ments of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.
Cite
Text
Vapnik. "Principles of Risk Minimization for Learning Theory." Neural Information Processing Systems, 1991.Markdown
[Vapnik. "Principles of Risk Minimization for Learning Theory." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/vapnik1991neurips-principles/)BibTeX
@inproceedings{vapnik1991neurips-principles,
title = {{Principles of Risk Minimization for Learning Theory}},
author = {Vapnik, V.},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {831-838},
url = {https://mlanthology.org/neurips/1991/vapnik1991neurips-principles/}
}