Truncation Error Compensation in Kernel Machines
Why this work is in the frame
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Bibliographic record
Abstract
The analysis and prediction of time series data has played an important role for intelligent systems used in the area of cybernetics and human-machine interaction. Time series prediction is especially important in the case of unreliable communication of data acquired by intelligent systems. Computationally efficient kernel based regression algorithms have allowed for the prediction of non-linear relationships within time series data. In this paper, we present the smooth delta corrected kernel least mean square (SDC-KLMS) algorithm. The SDC-KLMS scales in linear time with the number of samples stored, hence making it computationally efficient. We present a theoretical motivation for our algorithm and we experimentally show how our approach overcomes a limitation imposed by the use of a finite storage buffer. Experiments with simulated, benchmark, and real world data were conducted to verify the accuracy of our algorithm.
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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