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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... (Update)
Context of citations to this paper: More
...is the prediction of a Gaussian mixture density. Without going too much into the theoretical motivation of averaging (see, e.g. 6] 23] [24], 7] 25] the advantage of averaging can be understood by noting that since V ( P ) 1 N 2 P N i=1 V (P i ) 2 N 2 P N i=1 P N j=i...
.... the learning algorithms on variants of the same data set or use different approaches to combine the derived classifiers [2, 11, 15, 16, 17, 26, 27, 35, 39]. Breiman [3] and LeBlanc and Tibshirani [19] for example, acknowledge the value of using multiple predictive models to...
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BibTeX entry: (Update)
V. Tresp and M. Taniguchi. Combining estimators using non-constant weighting functions. Advances in Neural Information Processing Systems 7, 1995. http://citeseer.ist.psu.edu/tresp95combining.html More
@inproceedings{ tresp95combining,
author = "Volker Tresp and Michiaki Taniguchi",
title = "Combining Estimators Using Non-Constant Weighting Functions",
booktitle = "Advances in Neural Information Processing Systems",
volume = "7",
publisher = "The {MIT} Press",
editor = "G. Tesauro and D. Touretzky and T. Leen",
pages = "419--426",
year = "1995",
url = "citeseer.ist.psu.edu/tresp95combining.html" }
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