An asymptotically optimal kernel combined classifier

Majid Mojirsheibani and Jiajie Kong

Statistics & Probability Letters, 2016, vol. 119, issue C, 91-100

Abstract: A kernel ensemble classifier is developed for accurate classification based on several initial classifiers. A data-driven choice of the smoothing parameter of the kernel is considered and the resulting classifier is shown to be asymptotically optimal. Therefore, the proposed combined classifier asymptotically outperforms each individual classifier.

Keywords: Kernel; Hamming distance; Smoothing parameter; Combined classifier (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:119:y:2016:i:c:p:91-100

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DOI: 10.1016/j.spl.2016.07.017

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