Improving SVM performance through data reduction and misclassification analysis with linear programming
Why this work is in the frame
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Bibliographic record
Abstract
In the dual optimization problem behind Support Vector Machine (SVM), each data point corresponds to a decision variable. Therefore, removing data points is equivalent to reducing the dimensionality of the dual problem, leading to a more efficient optimization process. We introduce linear programming models to determine whether two sets of points are linearly separable efficiently, compute the misclassification rate, and reduce the dimension of the optimization problems behind the SVM procedure. Data reduction can be conducted using a simple convexity property for the linearly separable case. The misclassification rate is a key indicator of the complexity of separating the two sets, providing valuable insights into the classification performance. Our approach combines SVM optimization with linear programming techniques to offer a comprehensive classification and complexity analysis framework.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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