Optimal combinations of pattern classifiers
Pattern Recognition LettersPublished 1 September 1995
Louisa Lam, Ching Y. Suen
Citations309
SJR quartileQ1
SJR score1.00
SNIP1.43
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TL;DR
A Bayesian formulation and a weighted majority vote (with weights obtained through a genetic algorithm) are implemented, and the combined performances of 7 classifiers on a large set of handwritten numerals are analyzed.
Abstract
To improve recognition results, decisions of multiple classifiers can be combined. We study the performance of combination methods that are variations of the majority vote. A Bayesian formulation and a weighted majority vote (with weights obtained through a genetic algorithm) are implemented, and the combined performances of 7 classifiers on a large set of handwritten numerals are analyzed.
Keywords
Computer Science
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