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Abstract: : Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This paper provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and the order statistics combiners introduced in this paper. We show that combining networks in output space reduces the variance of the... (Update)
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BibTeX entry: (Update)
Tumer K and Gosh J. Theoretical Foundations of Linear and Order Statistics Combiners for Neural Pattern Classifiers, Technical Report TR-95-02-98, Computer and Vision Research Center, University of Texas, Austin, 1995 http://citeseer.ist.psu.edu/tumer96theoretical.html More
@misc{ tumer95theoretical,
author = "K. Tumer and J. Gosh",
title = "Theoretical Foundations of Linear and Order Statistics Combiners for Neural
Pattern Classifiers",
text = "Tumer K and Gosh J. Theoretical Foundations of Linear and Order Statistics
Combiners for Neural Pattern Classifiers, Technical Report TR-95-02-98,
Computer and Vision Research Center, University of Texas, Austin, 1995",
year = "1995",
url = "citeseer.ist.psu.edu/tumer96theoretical.html" }
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