TR available on stacked density estimation

[Padhraic Smyth]

FTP-host: ftp.ics.uci.edu
FTP-filename: /pub/smyth/papers/stacking.ps.gz


The following paper is now available online at:
ftp://ftp.ics.uci.edu/pub/smyth/papers/stacking.ps.gz



title: STACKED DENSITY ESTIMATION

authors: Padhraic Smyth (UCI/JPL) and David Wolpert (NASA Ames)

Abstract:
In this paper, the technique of stacking, previously only used for
supervised learning, is applied to unsupervised
learning. Specifically, it is used for non-parametric multivariate
density estimation, to combine finite mixture model and kernel density
estimators. Experimental 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 single best model chosen by ``cheating" by looking at the data
used for independent testing.

(This paper will also appear at NIPS97)