Théorie des matrices aléatoires robustes et applications à la détection rad
Bibliographic record
Abstract
Cet article présente de récents résultats issus de la combinaison entre la théorie des matrices aléatoires et la théorie de l’estimation robuste appliquées à des problèmes de détection en radar. Plus précisément, afin de pallier les problèmes de grande dimension des données, nous nous intéressons à une version régularisée de l’estimateur de matrice de covariance de Tyler (Tyler, 1987 ; Pascal, Chitour et al., 2008). Nous montrons ainsi grâce à l’analyse statistique de ce dernier, i.e. l’étude de son comportement au premier et second ordre en régime de grande dimension (N=n ! c 2 (0; 1] quand N; n ! 1), qu’un détecteur optimal (au sens de la maximisation des performances de détection et/ou de régulation de fausses alarmes) peut être construit. Enfin, des simulations Monte-Carlo montrent la pertinence de cette approche avec la comparaison aux méthodes traditionnellement utilisées.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".