A lower bound on the performance of the quadratic discriminant function

Tristrom Cooke

Journal of Multivariate Analysis, 2004, vol. 89, issue 2, 371-383

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.

Keywords: Quadratic; discriminant; Lower; error; bound; Non-Gaussian (search for similar items in EconPapers)
Date: 2004
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