Rejection strategies for learning vector quantization
نویسندگان
چکیده
We present prototype-based classification schemes, e. g. learning vector quantization, with cost-function-based and geometrically motivated reject options. We evaluate the reject schemes in experiments on artificial and benchmark data sets. We demonstrate that reject options improve the accuracy of the models in most cases, and that the performance of the proposed schemes is comparable to the optimal reject option of the Bayes classifier in cases where the latter is available.
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تاریخ انتشار 2014