Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Abstract
Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Tech(cid:173) niques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models re(cid:173) duce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly dis(cid:173) carding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory(cid:173) based learning algorithms, but we also argue that it is applicable to any class of model selection problems.
Cite
Text
Maron and Moore. "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation." Neural Information Processing Systems, 1993.Markdown
[Maron and Moore. "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/maron1993neurips-hoeffding/)BibTeX
@inproceedings{maron1993neurips-hoeffding,
title = {{Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation}},
author = {Maron, Oded and Moore, Andrew W.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {59-66},
url = {https://mlanthology.org/neurips/1993/maron1993neurips-hoeffding/}
}