ML Anthology

Stacked Density Estimation

Padhraic Smyth, David Wolpert
NeurIPS 1997 pp. 668-674
/neurips/1997/smyth1997neurips-stacked/

Abstract

In this paper, the technique of stacking, previously only used for supervised learning, is applied to unsupervised learning. Specifi(cid:173) cally, it is used for non-parametric multivariate density estimation, to combine finite mixture model and kernel density estimators. Ex(cid:173) perimental results on both simulated data and real world data sets clearly demonstrate that stacked density estimation outperforms other strategies such as choosing the single best model based on cross-validation, combining with uniform weights, and even the sin(cid:173) gle best model chosen by "cheating" by looking at the data used for independent testing.

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Cite

Text

Smyth and Wolpert. "Stacked Density Estimation." Neural Information Processing Systems, 1997.

Markdown

[Smyth and Wolpert. "Stacked Density Estimation." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/smyth1997neurips-stacked/)

BibTeX

@inproceedings{smyth1997neurips-stacked,
  title     = {{Stacked Density Estimation}},
  author    = {Smyth, Padhraic and Wolpert, David},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {668-674},
  url       = {https://mlanthology.org/neurips/1997/smyth1997neurips-stacked/}
}