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| 법의학적 우도비× | 텍스트 분류× | |
|---|---|---|
| 분야≠ | 법과학 | 텍스트 마이닝 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2004 | — |
| 창시자≠ | Colin Aitken & Franco Taroni | — |
| 유형≠ | Bayesian evidence evaluation model | Supervised NLP classification task |
| 원전≠ | Aitken, C. G. G., & Taroni, F. (2004). Statistics and the Evaluation of Evidence for Forensic Scientists (2nd ed.). Wiley. ISBN: 978-0-470-84367-3 | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| 별칭 | Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı | text categorization, document classification, topic classification, metin sınıflandırma |
| 관련≠ | 3 | 4 |
| 요약≠ | The Forensic Likelihood Ratio (LR) is a Bayesian framework for quantifying the weight of forensic evidence relative to two competing propositions — typically the prosecution and defence hypotheses. Formally developed and systematised by Colin Aitken and Franco Taroni in their 2004 Wiley monograph, the LR expresses how much more probable the observed evidence is under one hypothesis than under the other, providing the court with a single, interpretable number that separates the scientist's role from the fact-finder's role. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
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