Nonlinear Dynamic System Identification Based on Relevance Vector Machine
Why this work is in the frame
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Bibliographic record
Abstract
Based on Relevance Vector Machine, Sparse Bayesian, a kind of kernel method, which has the advantages such as its kernel functions without the restriction of Mercer condition, the relevance vectors automatically determinated, and smaller kernel functions, the smoothness priors restriction on Relevance Vector Machine (RVM) is suggested.The algorithmfast marginal likelihood maximization for sparse Bayesian modelsis applied to solve relevance vectors effectively, and improving the generalization of identification is also improved. The Cross Validation is adopted to determine the kernel parameter.The suggested method avoids the problem of difficultly determining the model structure by the Support Vector Machine for nonlinear dynamic system identification. Comparing with the Support Vector Machine, a quite simpler model structure is obtained. The result shows that the Relevance Vector Machine applied for nonlinear dynamic system identification achieves a better performance.
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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 | 0.001 |
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