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 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 | 0.001 | 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 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".