Abstract
Combining classifiers using Bayesian formalism deals with a high dimensional probability distribution composed of a class and the decisions of classifiers. Thus product approximation is needed for the probability distribution. Bayes error rate is upper bounded by the conditional entropy of the class and decisions, so the upper bound should be minimized for raising the class discrimination. By considering the dependency between class and decisions, dependency-based product approximation is proposed in this paper together with its related combination method. The proposed method is evaluated with the recognition of unconstrained handwritten numerals.
This research was financially supported by Hansung University in the year of 2004.
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Kang, HJ. (2004). Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_11
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DOI: https://doi.org/10.1007/978-3-540-25966-4_11
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