A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. The authors are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. They address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. They first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic …
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Oak Ridge National Lab., TN (United States)
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Tennessee
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A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. The authors are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. They address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. They first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic classes, they present a method based on error set estimation such that the performance guarantee of fusing all classifiers is at least as good as that of fusing any subset of classifiers.
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