A Data-Reuse Regularized Recursive Least-Squares Adaptive Filtering Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recursive least-squares (RLS) algorithm is very popular for adaptive filtering applications, mainly due to its fast convergence rate. The regularized version of this algorithm owns improved robustness features, especially in noisy environments. On the other hand, this robust behavior could influence the tracking capability of the algorithm and its overall convergence performance. To reach a proper balance between these performance criteria, a data-reuse regularized RLS algorithm is proposed in this paper. The specific data-reuse parameter of the resulting algorithm can be used as an additional control mechanism, thus reducing the influence of tuning the main convergence parameter of the RLS filter, which is the forgetting factor. Simulation results obtained in the framework of echo cancellation sustain the performance characteristics.
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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