Système de classification à deux niveaux de décision combinant approche par modélisation et machines à vecteurs de support
Bibliographic record
Abstract
The motivation of this work is based on two key observations. First, the classification algorithms can be separated into \ntwo main categories : discriminative and model-based approaches. Second, two types of patterns can generate \nproblems : ambiguous patterns and outliers. While, the first approach tries to minimize the first type of error, but cannot \ndeal effectively with outliers, the model-based approaches make the outlier detection possible, but are not sufficiently \ndiscriminant. Thus, we propose to combine these two different approaches in a two-stage classification system \nembedded in a probabilistic framework. In the first stage we pre-estimate the posterior probabilities with a \nmodel-based approach and we re-estimate only the highest probabilities with appropriate Support Vector Machine (SVM) in the second stage. Another advantage of this combination is to reduce the principal burden of SVM : the \nprocessing time necessary to make a decision. Finally, the first experiments on the benchmark database MNIST have \nshown that our dynamic classification process allows to maintain the accuracy of SVMs, while decreasing complexity \nby a factor 8.7 and making the outlier rejection available.
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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 | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".