Incorporating Training Errors for Large Margin HMMS Under Semi-Definite Programming Framework
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Bibliographic record
Abstract
In this paper, we study how to incorporate training errors in large margin estimation (LME) under semi-definite programming (SDP) framework. Like soft-margin SVM, we propose to optimize a new objective function which linearly combines the minimum margin among positive tokens and an average error function of all negative tokens. The new method is named as soft-LME. It is shown the new soft-LME problem can still be converted into an SDP problem if we properly define the average error function of all negative tokens based on their discriminative functions. Some preliminary results on TIDIGITS show that the soft-LML/SDP method yields modest performance gain when training error rates are significant. Moreover, it is also shown that the soft-LML/SDP can achieve much faster convergence for all cases which we have investigated.
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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 | 0.002 | 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 | 0.000 |
| 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