Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Error-correcting output codes (ECOC) are a successful technique to combine a set of binary classifiers for multi-class learning problems. However, in traditional ECOC framework, all the base classifiers are trained independently according to the defined ECOC matrix. In this paper, we reformulate the ECOC models from the perspective of multi-task learning, where the binary classifiers are learned in a common subspace of data. This novel model can be considered as an adaptive generalization of the traditional ECOC framework. It simultaneously optimizes the representation of data as well as the binary classifiers. More importantly, it builds a bridge between the ECOC framework and multitask learning for multi-class learning problems. To deal with complex data, we also present the kernel extension of the proposed model. Extensive empirical study on 14 data sets from UCI machine learning repository and the USPS handwritten digits recognition application demonstrates the effectiveness and efficiency of our model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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