A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals
Bibliographic record
Abstract
A new optimized algorithm for the learning process suitable for hardware implemented Winner Takes Most Kohonen Neural Network (KNN) has been proposed in the paper. In networks of this type a neighborhood mechanism is used to improve the convergence properties of the network by decreasing the quantization error. The proposed technique bases on the observation that the quantization error does not decrease monotonically during the learning process but there are some ‘activity’ phases, in which this error decreases very fast and then the ‘stagnation’ phases, in which the error does not decrease. The stagnation phases usually are much longer than the activity phases, which in practice means that the network makes a progress in training only in short periods of the learning process. The proposed technique using a set of linear and nonlinear filters detects the activity phases and controls the neighborhood R in such a way to shorten the stagnation phases. As a result, the learning process may be 16 times faster than in the classic approach, in which the radius R decreases linearly. The intended application of the proposed solution will be in Wireless Body Sensor Networks (WBSN) in classification and analysis of the EMG and the ECG biomedical signals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".