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Issue No.02 - February (2002 vol.24)
pp: 281-286
ABSTRACT
<p><b>Abstract</b>—We look at a single point in the feature space, two classes, and <tmath>$L$</tmath> classifiers estimating the posterior probability for class <tmath>$\omega_1$</tmath>. 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.</p>
INDEX TERMS
Classifier combination, theoretical error, fusion methods, order statistics, majority vote, independent classifiers.
CITATION
Ludmila I. Kuncheva, "A Theoretical Study on Six Classifier Fusion Strategies", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 2, pp. 281-286, February 2002, doi:10.1109/34.982906