A semiparametric density estimation approach to pattern classification

A new multivariate density estimator suitable for pattern classifier design is proposed. The data are first transformed so that the pattern vector components with the most non-Gaussian structure are separated from the Gaussian components. Nonparametric density estimation is then used to capture the non-Gaussian structure of the data while parametric Gaussian conditional density estimation is applied to the rest of the components. Both simulated and real data sets are used to demonstrate the potential usefulness of the proposed approach.

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