Application of Support Vector Machines to a Small-Sample Prediction
Why this work is in the frame
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Bibliographic record
Abstract
The support vector machines (SVMs) is one kind of novel small-sample machine learning methods based on solid theoretical background. Highly nonlinear regression and classification are their two applications. Different from conventional statistics methods, the SVMs employs the structural risk minimizing principle, which leads to high predication precision. For this method is not essentially related to probability measure and Law of Large Numbers, the final decision function is only determined by a small fraction of sample, called support vectors. Consequently, the complexity of computation only depends on the number of support vectors rather than the dimensions of the original sample space. In most occasions of oil and gas development, only small samples are available to predict the results of one measure. Introduction of SVMs into these applications can significantly improve prediction precision.
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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 | 0.001 |
| 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