Discriminative kernel learning in ambiguity domain
Bibliographic record
Abstract
Research in stochastic signal analysis is targeted towards two main objectives: (i) to obtain an overall dimensionality reduction and (ii) to provide reasonable characteristic estimates for quantification applications. Owing to the improved performance characteristics, time-frequency (TF) transformation tools are commonly used for such analysis. In this article, we propose a one-step characterization approach that exploits the collective advantages of TF analysis and discriminative kernels in the intermediate ambiguity domain (AD) for non-stationary signal analysis. Here, a machine learning kernel is used to suitably model the AD-map, following which certain robust AD-based features are extracted from the signal- and cross-term (generated during TF transformation) components. The novelty of the work is also geared towards finding out the usefulness of cross-terms for non-stationary signal classification applications. The proposed technique is evaluated for a multiclass quantification problem using one of the challenging stochastic triangular waveform datasets. Obtained misclassification accuracies are close to the theoretical minimum, previously reported using a Bayes classifier. Results indicate that this scheme shows great potential and can be extended in design of robust tools for real-life signal non-stationary analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".