MLLGSTAug 20, 2024

Asymptotic Classification Error for Heavy-Tailed Renewal Processes

arXiv:2408.10502v21 citationsh-index: 4
Originality Synthesis-oriented
AI Analysis

This work addresses classification challenges for point process data, which is common in various disciplines, but it is incremental as it builds on recent studies in this emerging area.

The authors tackled the problem of classifying renewal processes, particularly those with heavy-tailed inter-arrival times, by deriving asymptotic expressions for the Bhattacharyya bound on misclassification error probabilities.

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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