Particle Swarm Optimization of Fuzzy ARTMAP Parameters
Why this work is in the frame
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Bibliographic record
Abstract
In this paper a particle swarm optimization (PSO)-based training strategy is introduced for fuzzy ARTMAP that minimizes generalization error while optimizing parameter values. Through a comprehensive set simulations, it has been shown that this training strategy allows fuzzy ARTMAP to achieve a significantly lower generalization error than when it uses typical training strategies. Furthermore, the PSO strategy eliminates degradation of generalization error due to overtraining resulting from the training set size, number of training epochs, and data set structure. Overall results obtained with the PSO strategy reveal the importance of optimizing parameters and weights using a consistent objective function. In fact, the parameters found using this strategy vary significantly according to, e.g., training set size and data set structure, and always differ considerably from the popular choice of parameters that allows to minimize resources.
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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 | 0.001 | 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