A Hybrid Approach Based on SVM and Bernoulli Mixture Model for Binary Vectors Classification
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Bibliographic record
Abstract
In the last decades, the development of generative/discriminative approaches for classifying different kinds of data has attracted scholars' attention. Considering the strengths and weaknesses of both approaches, several hybrid learning approaches which combined the desirable properties of both have been developed. Our goal in this paper is to combine Support Vector Machines (SVMs), as a powerful classification tool, and Bernoulli mixture model in order to classify binary data. We propose using Bernoulli mixture model for generating probabilistic kernels for SVM based on information divergence. These kernels make intelligent use of unlabeled binary data to achieve good data discrimination. We demonstrate the merits of the proposed hybrid learning approach for the problem of classifying binary and texture images.
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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 | 0.000 |
| 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