Combining Estimators Using Non-Constant Weighting Functions

Combining Estimators Using Non-Constant Weighting Functions (1995)

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by Volker Tresp , Michiaki Taniguchi

Venue:	Advances in Neural Information Processing Systems 7
Citations:	56 - 4 self

BibTeX

@INPROCEEDINGS{Tresp95combiningestimators,
    author = {Volker Tresp and Michiaki Taniguchi},
    title = {Combining Estimators Using Non-Constant Weighting Functions},
    booktitle = {Advances in Neural Information Processing Systems 7},
    year = {1995},
    pages = {419--426},
    publisher = {MIT Press}
}

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Abstract

This paper discusses the linearly weighted combination of estimators in which the weighting functions are dependent on the input. We show that the weighting functions can be derived...

Citations

665	Adaptive mixtures of local experts - Jacobs, Jordan, et al. - 1991
264	Classification and Regression - Breiman, Friedman, et al. - 1984
70	Improving Regression Estimation: Averaging methods for variance reduction with extensions to general convex measure optimization - Perrone - 1993
50	The meta-pi network: building distributed knowledge representations for robust pattern recognition - Hampshire, Waibel - 1989
47	Some solutions to the missing feature problem in vision - Ahmad, Tresp - 1993
42	Training Neural Networks with Deficient Data - Tresp, Ahmad, et al. - 1994
28	A comparison of some error estimates for neural network models - Tibshirani - 1994
13	Filter selection model for motion segmentation and velocity integration - Nowlan, Sejnowski - 1994
10	Bias, variance and the combination of estimators; the case of linear least squares - Meir - 1994
1	Training neural networks with de data - Tresp, Ahmad, et al. - 1994