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A theoretical study on six classifier fusion strategies
Abstract
We look at a single point in feature space, two classes, and L classifiers estimating the posterior probability for class /spl omega//sub 1/. Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.
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Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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Metadata
- Authors
- Kuncheva, L. I.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002
- Volume
- 24
- Issue
- 2
- Pages
- 281-286
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2160767978
- Cited by
- 720
- References
- 1
- FWCI
- 28.44
- Type
- journal article
- Language
- en
- Open access
- closed
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