Incremental Learning of Stochastic Grammars with Graphical EM in Radar Electronic Support
Bibliographic record
Abstract
Although stochastic context-free grammars (SCFGs) appear promising for recognition of radar emitters, and for estimation of their level of threat in radar electronic support (ES) systems, well-known techniques for learning their production rule probabilities are computationally demanding, and cannot efficiently reflect changes in operational environments. Some techniques have been proposed for fast learning of SCFGs probabilities, yet, of those, only the HOLA technique can perform learning incrementally. In this paper, two incremental versions of the graphical EM (gEM) technique are proposed. The incremental gEM (igEM) and on-line incremental gEM (oigEM) allow for adapting production rule probabilities from new data, without having to retrain from the start on all accumulated training data. These new techniques are compared to HOLA using radar signal data. An experimental protocol has been defined such that the impact on performance of factors like the size of new data blocks for incremental learning, and the level of ambiguity of MFR grammars, may be observed. Results indicate that, contrary to HOLA, incremental learning of training data blocks with igEM and oigEM provides the same level of accuracy as learning from all cumulative data from scratch, even for small data blocks. As expected, incremental learning significantly reduces the overall time and memory complexities. Finally, it appears that while the computational complexity and memory requirements of igEM and oigEM may be greater than that of HOLA, they both provide a higher level of accuracy.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".