Asymptotic Classification Error for Heavy-Tailed Renewal Processes

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters Pub Date : 2025-09-18 DOI:10.1109/LSP.2025.3611709

Xinhui Rong;Victor Solo

引用次数: 0

Abstract

Despite the widespread occurrence of classification problems and the increasing collection of point process data across many disciplines, study of error probability for point process classification only emerged very recently. Here, we consider classification of renewal processes. We obtain asymptotic expressions for the Bhattacharyya bound on misclassification error probabilities for heavy-tailed renewal processes.

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重尾更新过程的渐近分类误差

尽管分类问题在许多学科中广泛存在，并且点过程数据的收集也在不断增加，但对点过程分类的错误概率的研究直到最近才出现。在这里，我们考虑更新过程的分类。得到了重尾更新过程误分类概率的Bhattacharyya界的渐近表达式。

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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来源期刊

IEEE Signal Processing Letters 工程技术-工程：电子与电气

CiteScore

7.40

自引率

12.80%

发文量

339

审稿时长

2.8 months

期刊介绍： The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.