L/sub p/ norm design of weighted order statistic filters
Bibliographic record
Abstract
This paper addresses the problem of designing weighted order statistic (WOS) filters by employing an objective function given as the L/sub p/ norm of the error between the desired signal and the estimated one. The conventional design of WOS filters uses a mean absolute error (MAE) objective function, and as such, it is a special case of the general, L/sub p/ norm based design, developed here. It is shown that in stack filtering, the L/sub p/ norm can be expressed as a linear combination of the decision errors incurred by the Boolean operators at each level of the stack filter architecture. Based on this formulation of the L/sub p/ norm, both nonadaptive and adaptive algorithms for the design of L/sub p/ WOS filters are developed. A design example is considered, to illustrate the performance of the designed L/sub p/ WOS filters with different values of p. The simulation results show that the L/sub p/ WOS filters with p/spl ges/2 are capable of removing more impulsive noise compared with the conventional MAE WOS filters.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".