Renewable <i>ℓ</i> <sub>1</sub> -Regularized Linear Support Vector Machine with High-Dimensional Streaming Data
Why this work is in the frame
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Bibliographic record
Abstract
The rapid growth of modern data collection methods brings new challenges for existing classification problems and the storage of huge datasets in memory. The need to develop online update methods is becoming increasingly pressing. In this paper, we study the renewable estimation process for a linear support vector machine (SVM) in high-dimensional online settings. The proposed renewable estimation process, which includes online l1-regularized and online debiased procedures, is feasible for handling high-dimensional streaming data since the online estimators are updated by integrating current new data batches with summary statistics of historical data, rather than re-accessing the entire raw dataset. Theoretically, we prove the convergence rates of the proposed online estimators under mild conditions. Numerical studies confirm the effectiveness of the proposed methods. Supplementary materials for this paper are available online.
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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 | 0.000 | 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