Bibliographic record
Abstract
Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data. The self-organizing map algorithm is heuristic in nature and will almost always converge. Since self-organizing map may be trapped in a local optimum, so we introduce momentum into the learning process thus the movement of a neuron may jump over local optimum. We expect this will be similar to the learning of neurons in back-propagation with momentum. Like the learning process in back-propagation, the timing for updating the amount of movement of a neuron is either batch mode or incremental mode. However, due to the neighborhood function, the movement of a non-winner neuron is relatively small as compare to when it is a winner. So when deciding the momentum, the previous movement of a neuron needs special consideration. Experiment result show that adding momentum to self-organizing map considerably contributes to the acceleration of the convergence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".