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摘要
This paper presents a novel pattern classification approach - a kernel and Bayesian discriminant based classifier which utilizes the distribution characteristics of the samples in each class. A kernel combined with Bayesian discriminant in the subspace spanned by the eigenvectors which are associated with the smaller eigenvalues in each class is adopted as the classification criterion. To solve the problem of the matrix inverse, the smaller eigenvalues are substituted by a small threshold which is decided by minimizing the training error in a given database. Application of the proposed classifier to the issue of handwritten numeral recognition demonstrates that it is promising in practical applications. Published by Elsevier Ltd.
