A Study Of Backpropagation, Counterpropagation, And Adaptive Resonance Theory Neural Network Models
Bibliographic record
Abstract
This paper presents the effects of varying critical parameters of the following important models: (i) Backpropagation (BP), (ii) Counterpropagation Network (CPN), and (iii) Adaptive Resonance Theory (ARTl). The input patterns consist of six sets of twelve 125-element binary vectors which are formed by projections of letters and characters represented by 7x5 binary pixels, and are similar to other patterns such as speech. The first set of patterns is undistorted, while the remaining sets of patterns are progressively distorted by random noise. By varying the number of hidden neurons, optimum ranges of the hidden neurons are found for the BP and CPN models. For the ARTl model, the impact of the vigilance parameter on the number of categories and overlaps, as well as on the stability of the long-term memory with two different sequencing of patterns was studied.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".