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Issue No.02 - February (2002 vol.24)
pp: 281-286
<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>
Classifier combination, theoretical error, fusion methods, order statistics, majority vote, independent classifiers.
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
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