Quantized kernel least mean mixed-norm algorithm
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Bibliographic record
Abstract
Quantized kernel least mean square (QKLMS) algorithm is an effective up-to-date adaptive nonlinear learning algorithm which also has good performance for kernel structure growing control. It achieves good results under Gaussian noise environment. In this paper, a new algorithm, quantized kernel least mean mixed norm (QKLMMN), is proposed for adaptive nonlinear learning with non-Gaussian additive noise statistical distribution models (including combination). As an alternative of conventional squared error criteria, mixed-norm criteria is utilized for our algorithm. A comprehensive convergence analysis is carried out. Experiments for nonlinear time series prediction and nonlinear system identification are conducted. Experimental results verified the effectiveness and superiority of our proposed algorithm compared with other kernel based adaptive nonlinear learning algorithms under non-Gaussian noise environment.
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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