U-statistics for estimating performance metrics in forensic handwriting analysis

Fuglsby, Cami; Saunders, Christopher P.; Buscaglia, JoAnn

doi:10.1080/00949655.2020.1715406

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The main result regarding to generalization bounds in this setting is manifested in next section, the proof of this result relies on the techniques of U -statistics and its applications which can be found in Fuchs et al [22], Bouzebda and Nemouchi [23], Fuglsby et al [24], Privault and Serafin [25], Bachmann and Reitzner [26], and Garg and Dewan [27], and we skip the details here.…”

Section: B Multi-dividing Ontology Algorithm By Maximizing Auc Measurementioning

confidence: 99%

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Zhu

Hua

2020

IEEE Access

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The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of U-statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications. INDEX TERMS small ontology, multi-dividing ontology learning, similarity measure I. INTRODUCE OF ONTOLOGY

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Section: B Multi-dividing Ontology Algorithm By Maximizing Auc Measurementioning

confidence: 99%

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Zhu

Hua

2020

IEEE Access

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Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods

Ishihara

Carne

2022

Forensic Science International

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U-statistics for estimating performance metrics in forensic handwriting analysis

Cited by 2 publications

References 17 publications

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods

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