Analyse automatique de donnees par support vector machines non supervises
Bibliographic record
Abstract
Cette dissertation presente un ensemble d'algorithmes visant a en permettre un usage rapide, robuste et automatique des « Support Vector Machines » (SVM) non supervises dans un contexte d'analyse de donnees. Les SVM non supervises se declinent sous deux types algorithmes prometteurs, le « Support Vector Clustering » (SVC) et le « Support Vector Domain Description » (SVDD), offrant respectivement une solution a deux problemes importants en analyse de donnees, soit la recherche de groupements homogenes (« clustering »), ainsi que la reconnaissance d'elements atypiques (« novelty/abnomaly detection ») a partir d'un ensemble de donnees. Cette recherche propose des solutions concretes a trois limitations fondamentales inherentes a ces deux algorithmes, notamment 1) l'absence d'algorithme d'optimisation efficace permettant d'executer la phase d'entrainement des SVDD et SVC sur des ensembles de donnees volumineux dans un delai acceptable, 2) le manque d'efficacite et de robustesse des algorithmes existants de partitionnement des donnees pour SVC, ainsi que 3) l'absence de strategies de selection automatique des hyperparametres pour SVDD et SVC controlant la complexite et la tolerance au bruit des modeles generes. La resolution individuelle des trois limitations mentionnees precedemment constitue les trois axes principaux de cette these doctorale, chacun faisant l'objet d'un article scientifique proposant des strategies et algorithmes permettant un usage rapide, robuste et exempt de parametres d'entree des SVDD et SVC sur des ensembles de donnees arbitraires.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".