1 - Algorithmes séquentiels pour l'analyse de données par méthodes à noyau
Bibliographic record
Abstract
In recent years, many methods of analysis and classification of data based on reproducing kernel Hilbert spaces have been developed. Most of these methods incorporate the fundamental principle dictated by Vapnik et al. in Support Vector Machines, which consists in extending linear algorithms to the non-linear case by using kernels. Kernel Fisher Discriminant (KFD) is one of these nonlinear methods which provides interesting results in many practical cases. However, the use of KFD requires storage and processing of matrices whose size equals the number of available data. This may be critical when the training set is large. This paper presents a sequential KFD algorithm which does not require the manipulation of large matrices. Sequential algorithms that fulfil the same requirements as KFD are also presented to perform Kernel Principal Component Analysis (KPCA) and Kernel Generalized Discriminant Analysis (KGDA).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".