An asymptotically optimal kernel combined classifier

被引：1

作者：

Mojirsheibani, Majid ^{[1
]}

Kong, Jiajie ^{[1
]}

机构：

[1] Calif State Univ Northridge, Dept Math, Northridge, CA 91330 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2016年 / 119卷

基金：

美国国家科学基金会;

关键词：

Kernel; Hamming distance; Smoothing parameter; Combined classifier; RANDOM FORESTS; REGRESSION; IMPROVE;

D O I：

10.1016/j.spl.2016.07.017

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：91 / 100

页数：10

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