针对具有验证延迟的数据流的预期贝叶斯分类法

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Journal of Applied Statistics Pub Date : 2024-02-21 DOI:10.1080/02664763.2024.2319222

Vera Hofer, Georg Krempl, Dominik Lang

引用次数: 0

摘要

大多数现有的非稳态数据流自适应分类算法都需要最近的标签数据来更新。而这种近期标签往往是缺失的。对于数据流分类来说，这就需要有新的标签。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Anticipative Bayesian classification for data streams with verification latency

Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classificatio...

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来源期刊

Journal of Applied Statistics 数学-统计学与概率论

CiteScore

3.40

自引率

0.00%

发文量

126

审稿时长

6 months

期刊介绍： Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.