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/}
}