SVM Classifier Approach to Enumerate Directional Signals Impinging on an Array of Sensors
Bibliographic record
Abstract
The support vector machine classification method is used for source enumeration; i.e., estimating the number of sources contributed in generation of the signals received by the sensors of a passive array. The main motivation comes from the problems where the data model is not completely known, or the model is subject to some changes due to real environmental conditions, (i.e., the case of data model mismatch). With no model mismatch, because we know a-priori about the generative model of the data, the traditional statistical signal processing techniques yield better results compared to general machine learning techniques like SVM (which try to learn everything from training data samples, without any use of their generative data model, and also by employing a simple geometric structure). Monte-Carlo test results show the potential of the SVM to compete with statistical signal processing in this particular application. Specifically, the SVM has a slightly better performance in the case of model mismatch, (sensor gain perturbations, sensor phase perturbation, and for spatially correlated array noise), compared to traditional enumeration methods MDL and AIC, at very low SNR values
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".