Abstract
In this paper, we introduce the likelihood accordance function (LA function for short), which is defined to characterize the accordance of a new observation to be classified with training samples. The LA classifier is then constructed using the ratio of LA functions. It is shown that, the LA functions are invariant under orthogonal linear transformations, while LA classifier is invariant under non-degenerate linear transformations. Moreover, the asymptotic optimality of LA classifier is obtained. At last, several simulations illustrated that the new LA classifier performs much better than the traditional classifiers.
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