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A lower bound on the performance of the quadratic discriminant function

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  • Cooke, Tristrom

Abstract

The quadratic discriminant function is often used to separate two classes of points in a multidimensional space. When the two classes are normally distributed, this results in the optimum separation. In some cases however, the assumption of normality is a poor one and the classification error is increased. The current paper derives an upper bound for the classification error due to a quadratic decision surface. The bound is strict when the class means and covariances and the quadratic discriminant surface satisfy certain specified symmetry conditions.

Suggested Citation

  • Cooke, Tristrom, 2004. "A lower bound on the performance of the quadratic discriminant function," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 371-383, May.
    • Handle: RePEc:eee:jmvana:v:89:y:2004:i:2:p:371-383
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    References listed on IDEAS

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    1. Cooke, Tristrom & Peake, Michael, 2002. "The Optimal Classification Using a Linear Discriminant for Two Point Classes Having Known Mean and Covariance," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 379-394, August.
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