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Abstract: Recently methods for combining estimators have been popular and enjoyed considerable success. Typically, one obtains several models by considering different model architectures (e.g. linear or nonlinear) or uses different partitions of the data (overlapping or nonoverlapping) for estimating the model. In a final step the outputs of each model are combined, either by assigning fixed weights a priori (e.g. equal weighting) or be estimating these weights from data as well. Here we propose a... (Update)
Context of citations to this paper: More
...2500 Stability ( No. examples CMM Bagging C4.5 Figure 4: Average stability as a function of the number of artificial examples. [33] combined multiple neural networks into one by averaging parameters, with the goal of achieving performance time computational savings; the...
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BibTeX entry: (Update)
J. Utans. Weight averaging for neural networks and local resampling schemes. In Proceedings of the AAAI-96 Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, pages 133--138, Portland, OR, 1996. AAAI Press. http://citeseer.ist.psu.edu/utans96weight.html More
@misc{ utans96weight,
author = "J. Utans",
title = "Weight averaging for neural networks and local resampling schemes",
text = "J. Utans. Weight averaging for neural networks and local resampling schemes.
In Proceedings of the AAAI-96 Workshop on Integrating Multiple Learned Models
for Improving and Scaling Machine Learning Algorithms, pages 133--138, Portland,
OR, 1996. AAAI Press.",
year = "1996",
url = "citeseer.ist.psu.edu/utans96weight.html" }
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