Unsupervised and semi-supervised multi-class support vector machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present new unsupervised and semi-supervised training algorithms for multi-class support vector machines based on semidefinite programming. Although support vector machines (SVMs) have been a dominant machine learning technique for the past decade, they have generally been applied to supervised learning problems. Developing unsupervised extensions to SVMs has in fact proved to be difficult. In this paper, we present a principled approach to unsupervised SVM training by formulating convex relaxations of the natural training criterion: find a labeling that would yield an optimal SVM classifier on the resulting training data. The problem is hard, but semidefinite relaxations can approximate this objective surprisingly well. While previous work has concentrated on the two-class case, we present a general, multi-class formulation that can be applied to a wider range of natural data sets. The resulting training procedures are computationally intensive, but produce high quality generalization results.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it