Bibliographic record
Abstract
We examine a new general machine learning model for binary classification by consideringthe expected weighted output of a parameterized predictor, where the weighting belongsto an RKHS of functions over the parameters. This weighting can be learned using variousalgorithms. One of them is Stochastic Functional Gradient Descent (SFGD), which itera-tively samples a parameter and some training data, and calculates an approximation of thefunctional gradient of the loss. Using the stability properties of the algorithm, we show thatconvergence is guaranteed, under mild assumptions, with rate O(1/√m) on the number ofexamples needed for learning. Further theoretical analysis, based on the Rademacher com-plexity of the proposed class of predictors, provides a similar bound on the generalizationerror. We also present three alternate learning algorithms, and a procedure for pruning themodel using the Lasso. We prove an error bound for the resulting sparse predictor. Finally,we run experiments using simple instantiations of the model to showcase its usability, andcompare the learning algorithms among one another, and to the state of the art.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".