Using Genetic Algorithm to Optimize Parameters of Support Vector Machine and Its Application in Material Fatigue Life Prediction
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Bibliographic record
Abstract
Support vector machine is a new kind of learning method based on solid theoretical foundation, but this method has the characteristic of sensitivity to parameter. According to this characteristic, this paper use genetic algorithm to optimize the parameters of SVM and cross validation is introduced to reduce the dependence of the parameters on the training samples. Through the analysis of fatigue data for the relevant literature, take the parameters of the best generalization ability as the final parameters and apply the obtained model (GA-SVR) in material fatigue life prediction. Compared with the conventional SVR model and PSO-SVR model, the mean square error and the square of correlation coefficient are used to verify the reliability and accuracy of the three models. The results show that, the GA-SVR model can predict the fatigue life of materials with high accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| 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