An Optimized Learning Algorithm Based on Linear Filters Suitable for Hardware implemented Self-Organizing Maps.
Bibliographic record
Abstract
Abstract. In this study, we present a fast and energy efficient learning algorithm suitable for Self-Organizing Maps (SOMs) realized in hardware. The proposed algorithm is an extension of the classical algorithm used in Kohonen SOM. It is based on the observation that the quantization error that is a typical quality measure of the learning process, does not decrease linearly along the learning process. One can observe the phases of the increased ‘activity’, during which the quantization error rapidly decreases, followed by ‘stagnation ’ phases, during which its values are almost the same. The activity phases occur just after decreasing the neighborhood radius, R. A set of finite impulse response (FIR) filters is used to detect both phases. This enables an automatic switching the radius R to a smaller value that shorts a given stagnation phase and starts a new activity phase. Comprehensive investigations carried out by means of the software model of the SOM show that the learning process can be shorten even by 80-95% that allows for reduction of energy consumption even by 70-90%. 1
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".