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Abstract: Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its component neural networks is analyzed from the context of both regression and classification, which reveals that it may be better to ensemble many instead of all of the neural networks at hand. This result is interesting because at present, most approaches ensemble all the available neural networks for prediction. Then, in... (Update)
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BibTeX entry: (Update)
Z.-H. Zhou, J. Wu, and W. Tang, "Ensembling neural networks: many could be better than all," Artificial Intelligence, vol. 137, no. 1-2, pp.239-263, 2002. http://citeseer.ist.psu.edu/zhou02ensembling.html More
@article{ zhou02ensembling,
author = "Z.-H. Zhou, J. Wu, and W. Tang",
title = "Ensembling neural networks: Many could be better than all",
journal = "Artificial Intelligence, 2002, 137(1-2): 239-263",
volume = "137",
number = "1-2",
pages = "239-263",
year = "2002",
url = "citeseer.ist.psu.edu/zhou02ensembling.html" }
Citations (may not include all citations):98 Machine Learning (context) - Breiman, predictors - 1996
77 UCI repository machine learning database [httpwww (context) - Metz, of et al. - 1998
The graph only includes citing articles where the year of publication is known.
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