The naive Bayes classifier for functional data

被引：22

作者：

Zhang, Yi-Chen ^{[1
]}

Sakhanenko, Lyudmila ^{[1
]}

机构：

[1] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2019年 / 152卷

关键词：

Functional data; Functional classification; Functional common principal component; Naive Bayes classifier; Kernel methods; Density estimation; DISCRIMINANT-ANALYSIS; SPARSE; CONVERGENCE; DENSITY; RATES;

D O I：

10.1016/j.spl.2019.04.017

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：

In this article, we extended the approach of Dai et al. (2017) to several populations case and compared various other approaches on simulated and real data. The results show that the naive Bayes classifier for functional data is applicable to multi-category classification problems and has preferable finite-sample performance over competitors. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：137 / 146

页数：10

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